{"as_of":"2026-08-14T12:20:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5f088c92e838eada5afd852ebf2e00197299e20318f8fa6d7ecbc16aa06c221a","coverage":[{"denominator":84,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":84,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-13T02:03:42.456988Z","state":"measured"},{"denominator":84,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":84,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2605.11558/citation-record","integrity":"/paper/2605.11558/integrity","json":"/paper/2605.11558/citation-record.json","paper":"/paper/2605.11558"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"kaggle","venue":null,"work_id":"2c7eaf72-67b9-4e5d-b028-663fb461c73f","year":2017},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:7e181ef96e8fdb4209c4097ce7afaabc1e86e8c6adb5fb62b98513a787b1773b","observation_id":"920fd037-be4f-47af-aa17-c5bf3412561f","resolution":{"observed_at":"2026-05-13T13:52:51.770176Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"FICO Explainable Learning Challenge","venue":null,"work_id":"16f11cdc-7f92-4a07-a70e-6d2c8cbeada3","year":2018},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:5243885245b7f542f85e19f05585950410368172266d41a85dcb9b5b7b962755","observation_id":"dd872592-e52b-4fe2-a2cb-70c435d43af3","resolution":{"observed_at":"2026-05-13T13:52:51.699877Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1308.3432","last_updated":"2013-08-15T15:19:34Z","snapshot_observed_at":"2026-08-14T04:51:04.817737Z","submitted_at":"2013-08-15T15:19:34Z","title":"Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation","version":1},"cited_work":{"arxiv_id":"1308.3432","doi":"10.48550/arxiv.1308.3432","metadata_source":"pith","pith_arxiv_id":"1308.3432","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation","venue":"cs.LG","work_id":"1fe8c7c8-aff7-4b94-9096-e549d7e60789","year":2013},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"cited_paper":"/paper/1308.3432","citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:2893b38637751f56c42e8940138c712a88d9dbbfbd780c101fd277cbc998dd3f","observation_id":"05d75af4-b7c8-461c-80ac-2fd8bb723991","resolution":{"observed_at":"2026-05-13T02:07:07.894885Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-05-19T16:22:27.235199+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-19T16:22:27.235199+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Efficient activation function optimization through surrogate modeling.Advances in Neural Information Processing Systems, 36:6634–6661","venue":null,"work_id":"9a96c8dd-bdc4-4c2b-ad2b-8f33a1ffbe8d","year":2023},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:b4a7ffd390aeda2f723d136b92cfe786bf23e900d141a53eb3d5fd1ce5fd24e4","observation_id":"70a33501-e09a-4f02-9753-b4686100c076","resolution":{"observed_at":"2026-05-13T13:52:51.691443Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T16:57:25.332055Z","title":"Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901","venue":null,"work_id":"bd7ec542-9242-446e-955e-bb75e729be5d","year":1901},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:a78a6465a02147b2ea9f2e81fc7252824cd11741e291415325c92bfe29160390","observation_id":"af16e840-a523-43a7-bc85-884614282217","resolution":{"observed_at":"2026-05-13T13:52:51.695894Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Spiking deep convolutional neural networks for energy-efficient object recognition.International Journal of Computer Vision, 113(1):54–66","venue":null,"work_id":"5e400397-a858-49d8-b464-3cb50614f6a0","year":2015},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:0ebdd56073526cffcd2eef4198451a8efb4d6dcd6e6ed4f06f5b10e25739b318","observation_id":"294142f4-d4d7-4c5d-9acb-8406bad0791a","resolution":{"observed_at":"2026-05-13T13:52:51.681364Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Hashnet: Deep learning to hash by continuation","venue":null,"work_id":"d71987ef-7e7a-4e4b-bf23-b85edf3625a5","year":2017},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:ffda3c2b54df8519b353f2c6edb83d40cec25e5c819adb222355839c473d7214","observation_id":"3b1a5df7-43fc-4e5d-84d8-2a8d02571025","resolution":{"observed_at":"2026-05-13T13:52:51.718505Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Training for stable explanation for free.Advances in Neural Information Processing Systems, 37:3421–3457","venue":null,"work_id":"8bac0116-fdd1-47fc-9171-43afd25e8f4b","year":2024},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:3ab81ded555f0516504036d3ff1c4eb3af00b7f261249802181d68beb67d1d3c","observation_id":"1cc46c00-e4e8-4f46-b66a-3f3b0fc9c564","resolution":{"observed_at":"2026-05-13T13:52:51.676901Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Neural characteristic activation analysis and geometric parameteri- zation for relu networks.Advances in Neural Information Processing Systems, 37:97562–97586","venue":null,"work_id":"8b3283a8-df86-4e53-8a23-12b365889e52","year":2024},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:7125a9bf2170ea0d4447468e2d99803005e005b46e4d841c3e2ea72af86df302","observation_id":"feccdc70-1488-4aaf-ae82-a0e113797821","resolution":{"observed_at":"2026-05-13T13:52:51.570674Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.24432/c56s3t","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Cerdeira, F","venue":"UC Irvine","work_id":"e52fd096-13d3-48e0-bb48-303e2acb6feb","year":2018},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:f568469be3ec628a6bda654471f4c6bf45ef206394735493807e271f9174b9a2","observation_id":"127cf276-a6f2-4aa9-ba73-4169a54591ef","resolution":{"observed_at":"2026-05-13T02:07:07.283530Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1602.02830","last_updated":"2016-03-17T14:54:25Z","snapshot_observed_at":"2026-08-10T10:55:02.784121Z","submitted_at":"2016-02-09T01:01:59Z","title":"Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1","version":3},"cited_work":{"arxiv_id":"1602.02830","doi":"10.48550/arxiv.1602.02830","metadata_source":"pith","pith_arxiv_id":"1602.02830","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1","venue":"cs.LG","work_id":"73fcd90c-53bb-4d2e-87ef-284402d02867","year":2016},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"cited_paper":"/paper/1602.02830","citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:100bdd2665577e3917972c26edbfe75af88cafcda408489e561ef57977700e11","observation_id":"88780506-129a-42ec-847c-21206f3e2120","resolution":{"observed_at":"2026-05-13T02:07:07.875986Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing","venue":null,"work_id":"7f9aadd8-1bfc-45d0-912c-6523c255ff19","year":2015},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:871c61420fd9eea721a477b3163bb4283f7feda81a27ab2ff03138c344cec2be","observation_id":"c4e7e324-822e-4dd3-a101-ebaf1d469e66","resolution":{"observed_at":"2026-05-13T13:52:51.579613Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-08-13T14:19:26.598265Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":"2010.11929","doi":"10.1175/jcli-d-22-0357.1","metadata_source":"pith","pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","venue":"cs.CV","work_id":"e96730e3-129b-4db6-b981-15ab7932e297","year":2020},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:62c7679b6d88293dca5b47529ab64c7dc5b9ec2e2249b489db0c450442dd06d0","observation_id":"e40a1f05-1a46-4684-90dc-968bb1d1cd04","resolution":{"observed_at":"2026-05-13T02:07:07.988104Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.03382","last_updated":"2023-03-06T18:59:13Z","snapshot_observed_at":"2026-08-13T12:30:28.280557Z","submitted_at":"2023-03-06T18:59:13Z","title":"Globally Optimal Training of Neural Networks with Threshold Activation Functions","version":1},"cited_work":{"arxiv_id":"2303.03382","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2303.03382","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Globally optimal training of neural networks with threshold activation functions","venue":null,"work_id":"59a8b31f-ac68-4f68-bee7-a2db205e558f","year":2023},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"cited_paper":"/paper/2303.03382","citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:71a967898572f1ec14347566f90e20ad973c1f748a0f3ba7ca9cfe4ea3e30d12","observation_id":"39068377-a22e-4d05-b16b-c135627799bd","resolution":{"observed_at":"2026-05-13T02:07:07.939635Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Spikingjelly: An open-source machine learning infrastructure platform for spike-based intelligence.Science Advances, 9(40):eadi1480","venue":null,"work_id":"0f67687f-f809-4ba6-9c4c-849573d8ecd3","year":2023},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:6179b40e0baab8c3adddbe2abeaa512ec8078544f421c731a5a81508e3e2e89b","observation_id":"fd6264d6-3a38-42cb-b340-a6018e85a20d","resolution":{"observed_at":"2026-05-13T13:52:51.639146Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Deep residual learning in spiking neural networks.Advances in neural information processing systems, 34:21056–21069","venue":null,"work_id":"a20e8ddd-f642-4069-8af2-2ed114556de1","year":2021},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:be6300038a9eb79578dcc0a7b833ac98202721a29336f0ad0e3448c86b012aad","observation_id":"eb7ccfed-9ae4-4053-95b2-d4839d794248","resolution":{"observed_at":"2026-05-13T13:52:51.566074Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Craft: Concept recursive activation factorization for ex- plainability","venue":null,"work_id":"97356a4d-2415-4681-bd38-69566a57caf8","year":2023},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:566c67c7bfffea344d380f88b6154198a38ae6d59e0882d6fe09b6f286c4816b","observation_id":"754ec247-3bcd-4f65-bdcf-de945123fba2","resolution":{"observed_at":"2026-05-13T13:52:51.602039Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Towards automatic concept- based explanations.Advances in neural information processing systems, 32","venue":null,"work_id":"429a7b1b-a473-4e6d-8e53-66b1d796bed8","year":2019},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:f34332e7c0092b0bf3b74ec6488631f62d58c55aa0aa7b0153d8dce0218cc017","observation_id":"f0e3bf70-3ad1-470b-b0b8-d6df10aaaf34","resolution":{"observed_at":"2026-05-13T13:52:51.820969Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Understanding the difficulty of training deep feedfor- ward neural networks","venue":null,"work_id":"60435352-480a-42cd-a2df-15e28e87a7ff","year":2010},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:500c7e8d9241d5fb10dd47aab7888be816a93d7268a35b89643845d73939560b","observation_id":"c8587054-2966-4223-b9ab-67ec4ddfcf3c","resolution":{"observed_at":"2026-05-13T13:52:51.783414Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T19:05:31.256974Z","title":"Deep sparse rectifier neural networks","venue":null,"work_id":"b19da980-3fd7-4bbf-9cd1-a54524f43c0e","year":2011},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:1d12054d74936d1998dcc2f8823da77a7af862ac120ff9586b72efbd3140c463","observation_id":"a4351cab-21d5-45f2-94ae-b01969b27eda","resolution":{"observed_at":"2026-05-13T13:52:51.787336Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"On the impact of the activation function on deep neural networks training","venue":null,"work_id":"08b60756-be76-498b-b2fb-9357580668c1","year":2019},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:f6bad549fd5456d3d16d5dff3bea97d84e30832e1b50a252fd959fabdbccf486","observation_id":"92fd2883-5775-431f-8efc-4aa37edace9a","resolution":{"observed_at":"2026-05-13T13:52:51.791642Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T03:47:55.367441Z","title":"Deep residual learning for image recognition","venue":null,"work_id":"9a00f23b-71b4-4f99-a115-0d30a296f178","year":2016},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:4c90545d0fc0688e4f586a1b7dbda41d17366f65c4adcd77f3bfb7dbab17947f","observation_id":"7f30b2cf-4ba7-4ae7-a834-4898438f89da","resolution":{"observed_at":"2026-05-13T13:52:51.774800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1912.02781","last_updated":"2020-02-17T06:16:13Z","snapshot_observed_at":"2026-08-14T11:38:20.162100Z","submitted_at":"2019-12-05T18:18:10Z","title":"AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty","version":2},"cited_work":{"arxiv_id":"1912.02781","doi":"10.48550/arxiv.1912.02781","metadata_source":"arxiv_reference","pith_arxiv_id":"1912.02781","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"11 Published as a conference paper at ICLR 2025 Dan Hendrycks, Norman Mu, Ekin D Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshmi- narayanan","venue":"arXiv (Cornell University)","work_id":"53ff4aeb-86c1-40a5-b3c7-a790480998eb","year":1912},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"cited_paper":"/paper/1912.02781","citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:f13a41bad50b6171ce6f488ff460a5fcaea0b405bcce244efb71a8225e4b4cb9","observation_id":"88bec5a6-36b2-46b0-a1b7-25eedd3f3862","resolution":{"observed_at":"2026-05-13T02:07:08.017690Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T03:07:55.374505Z","title":"Lora: Low-rank adaptation of large language models.Iclr, 1(2):3","venue":null,"work_id":"80a84b35-042e-4846-80ca-177dbf9b1b1b","year":2022},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:df3c6f1aba884f92ad1a094dadeb89e3a6e264aee232c57064764f81e28c7422","observation_id":"f3c7e6e3-1c91-451e-b9de-03923da49cd2","resolution":{"observed_at":"2026-05-13T13:52:51.795022Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.04291","last_updated":"2024-05-15T13:55:12Z","snapshot_observed_at":"2026-08-13T15:50:33.443969Z","submitted_at":"2024-02-06T09:26:34Z","title":"BiLLM: Pushing the Limit of Post-Training Quantization for LLMs","version":2},"cited_work":{"arxiv_id":"2402.04291","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.04291","snapshot_observed_at":"2026-07-04T17:29:59.598269Z","title":"Billm: Pushing the limit of post-training quantization for llms","venue":null,"work_id":"5ad1f303-8e85-433c-9085-ff3267c2fb8c","year":2024},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"cited_paper":"/paper/2402.04291","citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:08c72bdcc55411daf8ddb7b1e769bd1c867f58dcd18a397a7232123988f9ca36","observation_id":"198e1cda-1994-40b0-8805-c112939a89ed","resolution":{"observed_at":"2026-05-13T02:07:07.889038Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Quan- tized neural networks: Training neural networks with low precision weights and activations","venue":null,"work_id":"f4b9be3d-a9b7-4025-b425-a21b5f0b7d24","year":2018},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:40ab3dc1bd034ae63781e14f1e0a427b701c305fbb4b9c575d8a248a85bcf7aa","observation_id":"1edf4ccb-3bc7-438e-ae37-e1f102e97950","resolution":{"observed_at":"2026-05-13T13:52:51.779435Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2512.16872","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"On the universal representation property of spiking neural networks","venue":null,"work_id":"dfe09fe1-b97c-4d8a-90ac-970e9f88dd4d","year":2025},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:e05a05cb96467f4fc446eed8d7d0e2144964c72c863f788e33f6cf0d3bbad0f3","observation_id":"7549d417-461b-4727-824c-48ed91382ad4","resolution":{"observed_at":"2026-05-13T02:07:07.907720Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"On the approximation of the step function by some sigmoid functions.Mathematics and Computers in Simulation, 133:223–234","venue":null,"work_id":"a9cdcb8b-d452-4594-904b-90fb537fdc96","year":2017},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:e4bb003379b16c36a0d52833f66a71688a87c91475a600ff5c02276c4f4a7728","observation_id":"4ea037db-f837-49ee-a3a0-9fa2ac9defe8","resolution":{"observed_at":"2026-05-13T13:52:51.807279Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Deep nonparametric regression on approximate manifolds: Nonasymptotic error bounds with polynomial prefactors.The Annals of Statistics, 51(2):691–716","venue":null,"work_id":"a1515fbc-3954-4f06-9529-f3b74756b4b1","year":2023},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:f4a4f841e261d8ef584df06c2916757a2b78e19bb52c898235db51519954fdb8","observation_id":"d68b8590-7fc7-46c4-ad74-c55a0e6f9716","resolution":{"observed_at":"2026-05-13T13:52:51.742182Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2510.07115","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Enhancing concept localization in clip-based concept bottleneck models.arXiv preprint arXiv:2510.07115","venue":null,"work_id":"e0290757-f92b-4d76-9bba-ccaa6b398abd","year":2025},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:64bd65f7ab0a86287628a1c8ea842e1c7c8e0a2aee98fd793e1d0c1bbb15d414","observation_id":"3da3ff81-8577-476f-85d6-c7afaa6ffb51","resolution":{"observed_at":"2026-05-13T02:07:07.964785Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)","venue":null,"work_id":"4ec86fa8-fe3e-4b06-8ab7-a9a88ccefa51","year":2018},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:4221ede08fc463a897747219963e014a8f5419ffea87139bd47396367fbf28bf","observation_id":"580a73db-b991-4ff8-a072-81e3c4ab5969","resolution":{"observed_at":"2026-05-13T13:52:51.588624Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Concept bottleneck models","venue":null,"work_id":"c28fc6cd-0d30-4aeb-a28c-c98c87afe24f","year":2020},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:6965fcdd32b3feb0da0daf0ca67e42b1732303cdc3f4470101ca122496be871c","observation_id":"34a9338c-03ac-4182-9807-fba9d9f83ca6","resolution":{"observed_at":"2026-05-13T13:52:51.561855Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"On the rate of convergence of fully connected deep neural network regression estimates.The Annals of Statistics, 49(4):2231 – 2249","venue":null,"work_id":"81d00aed-40e9-440d-a985-5c4f59c324e1","year":2021},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:3de56b68139539491887498cc62745f03ab5964d24857daf13b16bbdde03287b","observation_id":"bad49bdd-26d8-43b5-ab16-b683599ea67c","resolution":{"observed_at":"2026-05-13T13:52:51.733306Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.00110","last_updated":"2026-08-10T01:32:48Z","snapshot_observed_at":"2026-08-13T23:25:50.574824Z","submitted_at":"2025-04-30T18:25:05Z","title":"On the expressivity of deep Heaviside networks","version":2},"cited_work":{"arxiv_id":"2505.00110","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.00110","snapshot_observed_at":"2026-08-11T01:24:25.830181Z","title":"On the expressivity of deep heaviside networks","venue":null,"work_id":"09f70759-46a0-4ecb-ac51-a1d103fd81a8","year":2025},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"cited_paper":"/paper/2505.00110","citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:360474724c6d750c1f04d38e9dc862b3048b9889f550c2d1e18c0a9c5c0e93e0","observation_id":"1c2d7f87-7ae9-4c69-87d0-40ad5d896a79","resolution":{"observed_at":"2026-08-11T01:24:25.830181Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Posterior concentrations of fully-connected bayesian neural networks with general priors on the weights.Journal of Machine Learning Research, 26(94):1– 60","venue":null,"work_id":"dde29496-197c-4c4e-bd2b-38baf98778bb","year":2025},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:413dec5d5c0419d44314f238cec99f0f49755e3a5adefe48c0bb40c0f12a3d4e","observation_id":"48cf3d6b-e6a8-4c52-98b2-f195f6faf9f4","resolution":{"observed_at":"2026-05-13T13:52:51.652645Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.7717/peerjcs.633/fig-5","metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T11:47:03.723149Z","title":"Learning multiple layers of features from tiny images","venue":null,"work_id":"3807822d-12bd-4f6f-89ab-c3132c0cbfff","year":2009},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:3f5d4886e8957d65ba7558856ede92f278e42eed993c6f181dc5c2903342ab47","observation_id":"6e70f95a-cfdf-426c-8195-126bfa32e875","resolution":{"observed_at":"2026-05-13T13:52:51.728501Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Interpretable generative models through post-hoc concept bottlenecks","venue":null,"work_id":"db3edc78-948e-4163-9258-f10b3d9e8d1b","year":2025},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:477975f68324867ade39f69fe6b3d54705e2923a3fc8c41ea9d6d5fecaf28ff9","observation_id":"0cc01f46-caea-4ebc-9b8e-3e43989adc1d","resolution":{"observed_at":"2026-05-13T13:52:51.825290Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.00730","last_updated":"2019-02-02T14:48:16Z","snapshot_observed_at":"2026-07-06T07:30:44.870185Z","submitted_at":"2019-02-02T14:48:16Z","title":"Self-Binarizing Networks","version":1},"cited_work":{"arxiv_id":"1902.00730","doi":"10.48550/arxiv.1902.00730","metadata_source":"pith","pith_arxiv_id":"1902.00730","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Self- binarizing networks","venue":"cs.CV","work_id":"f7f105cb-9b2f-489c-ac21-19af30fc0103","year":2019},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"cited_paper":"/paper/1902.00730","citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:d37ea659dd2f29e050cbaa3f5afd9aa5c9d50da7887b9be142b76d10422e6bb7","observation_id":"e2e69532-e173-406e-9e10-d41d675c63a2","resolution":{"observed_at":"2026-05-13T02:07:07.971253Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Tiny imagenet visual recognition challenge.CS 231N, 7(7):3","venue":null,"work_id":"1c7eab6f-66a5-461e-bba4-db04d6c99fee","year":2015},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:63cda192fb23777a9fd1ae6e80ce0c83aa4ad7bf13316a14a3b541c5b4bb168b","observation_id":"f70b766f-006a-4867-80cf-5e8f7c8f0348","resolution":{"observed_at":"2026-05-13T13:52:51.756725Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Seeking interpretability and explainability in binary activated neural networks","venue":null,"work_id":"9a1d1df7-72bf-4f7c-a5e6-28571b925d13","year":2024},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:e74347a8f771ea65a7cc82158fe8ab8fbbb5e56d7bdb78b5a4d85410af58e05f","observation_id":"d7b4fb6b-d4e1-4487-85d0-b8670993626f","resolution":{"observed_at":"2026-05-13T13:52:51.812084Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-07T13:43:48.063556Z","title":"Differ- entiable spike: Rethinking gradient-descent for training spiking neural networks.Advances in neural information processing systems, 34:23426–23439","venue":null,"work_id":"d29e06c5-4917-4e56-97cd-03ab5eea146e","year":2021},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:ca7283c2a74d21ac7ed3958d3849d361ef58ee1c1cc3d15e7c800f1677b6cda9","observation_id":"4e45ce3d-d395-423c-89cd-5fd765de4722","resolution":{"observed_at":"2026-05-13T13:52:51.708847Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Bi-real net: Enhancing the performance of 1-bit cnns with improved representational capability and advanced training algorithm","venue":null,"work_id":"817b5ac2-55d5-48fa-b56c-ee5abffe4ee1","year":2018},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:d9420715847ba105f8bad62adc61fe9e38b5eec0463d436e281e3994d533a8cb","observation_id":"08021222-0a29-4cdc-86aa-0a64b4868ba2","resolution":{"observed_at":"2026-05-13T13:52:51.685837Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Deep network approximation for smooth functions.SIAM Journal on Mathematical Analysis, 53(5):5465–5506","venue":null,"work_id":"9ec29c69-3ba9-4de1-a059-98fdc2cbb329","year":2021},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:e172e452401820deb9bdb88a447536c5a561724c55b81b214ebe54981f18d2ea","observation_id":"122642f9-b15b-424e-96cc-594cf5a18640","resolution":{"observed_at":"2026-05-13T13:52:51.704190Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17764","last_updated":"2024-02-27T18:56:19Z","snapshot_observed_at":"2026-08-12T12:22:25.664363Z","submitted_at":"2024-02-27T18:56:19Z","title":"The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits","version":1},"cited_work":{"arxiv_id":"2402.17764","doi":"10.18653/v1/2023.acl-long.5","metadata_source":"pith","pith_arxiv_id":"2402.17764","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits","venue":"cs.CL","work_id":"4a49f413-bca9-4de8-8620-97aa4cb099f3","year":2024},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"cited_paper":"/paper/2402.17764","citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:5f01f891b6be2ebbe84ef1cfa0c2d6e911e797a54658b07f7341aff69c090d2d","observation_id":"6e03f6e3-717a-445a-902d-722081bb3e85","resolution":{"observed_at":"2026-05-17T20:11:43.638959Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-07T13:43:48.054634Z","title":"Networks of spiking neurons: the third generation of neural network models","venue":null,"work_id":"c7faedf4-b7a9-4e31-bb3b-fe098cc5ff9e","year":1997},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:f80a1582b5ce3c92f64c154005aec3562c4bbc355d3f07c18f416e75755cf92d","observation_id":"733df3cc-c9fc-49b1-a5b8-872f38fedec2","resolution":{"observed_at":"2026-05-13T13:52:51.575257Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Torchvision: Pytorch’s computer vision library","venue":null,"work_id":"f63a81d3-6597-45cb-aa3b-32859e0d7399","year":2016},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:b103541e193548b417ee378ea08391a1383404b39b9909090bc8970b7ba8bfc3","observation_id":"c1117672-1de4-4a9d-b94b-03526818e6c6","resolution":{"observed_at":"2026-05-13T13:52:51.761002Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T01:04:25.239779Z","title":"Can a suit of armor conduct electricity? a new dataset for open book question answering","venue":null,"work_id":"63cb3594-758b-4567-8c20-31940599075b","year":2018},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:4ea148a3a1ab59b3fb2c03aff163bd94b1125d08bb7bdb9d248772636e9455bf","observation_id":"e3274d6e-05b9-4a2c-b7fd-45aaafc4687b","resolution":{"observed_at":"2026-05-13T13:52:51.722922Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-07T13:43:47.991313Z","title":"Surrogate gradient learning in spiking neural networks.IEEE Signal Processing Magazine, 36(6):51–63","venue":null,"work_id":"0b959674-cfda-4631-88c3-ffbd92982b92","year":2019},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:75c6ea1a5bf46755df5d75c5ebd7f93e25ef0e42d2ab00494d15b571e1cec6cb","observation_id":"b4bc9f80-1e6d-4709-8abf-ceb9371b23f5","resolution":{"observed_at":"2026-05-13T13:52:51.714038Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Smooth function approximation by deep neural networks with general activation functions.Entropy, 21(7):627","venue":null,"work_id":"93affe71-9302-4f06-ad64-936e93144324","year":2019},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:aa78d0d87d100b8c702654dfd2e7e84f9f0e5e45abfc3d8cabfaeb38b565d5f9","observation_id":"1456e553-30ef-4c6e-bd92-79e2739dd88e","resolution":{"observed_at":"2026-05-13T13:52:51.634330Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.06129","last_updated":"2023-06-05T17:33:43Z","snapshot_observed_at":"2026-08-13T12:03:59.681802Z","submitted_at":"2023-04-12T19:27:09Z","title":"Label-Free Concept Bottleneck Models","version":2},"cited_work":{"arxiv_id":"2304.06129","doi":"10.48550/arxiv.2304.06129","metadata_source":"arxiv_reference","pith_arxiv_id":"2304.06129","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Label-free concept bottleneck models","venue":"arXiv (Cornell University)","work_id":"fd08f700-7785-4da8-8f9b-291d54cfd682","year":2023},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"cited_paper":"/paper/2304.06129","citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:9d819b1cd2edcb45fae8f2ac40d95c328061596e8a0b96c3f190fdb030463004","observation_id":"96246e06-4808-443c-9768-52d70089e6ef","resolution":{"observed_at":"2026-05-13T02:07:07.927970Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Optimal approximation of piecewise smooth functions using deep ReLU neural networks.Neural Networks, 108:296–330","venue":null,"work_id":"7e458dd9-2132-45ae-b3ff-f44cc15624c1","year":2018},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:a584fcb1854e2f0b902f9d2af7b08e62eb67b012b760c8431a5f9b9904ad8a60","observation_id":"dcb915c6-af1b-4222-a029-ba4edcc3dd72","resolution":{"observed_at":"2026-05-13T13:52:51.620871Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Binary neural networks: A survey.Pattern Recognition, 105:107281","venue":null,"work_id":"7df5f06f-36e9-4601-bd79-4e2eb9fe284f","year":2020},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:f28aa5c7ff9b6a313f7936f3c344db8db323eb01952626d13a22f214cc0c614d","observation_id":"3ac6f198-fd9b-404e-8ea2-285bd13656a4","resolution":{"observed_at":"2026-05-13T13:52:51.658798Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T17:17:26.778855Z","title":"Qwen3.5: Towards native multimodal agents, February 2026","venue":null,"work_id":"5fbed0ab-3251-4174-88cf-edabd29a7641","year":2026},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:857ed8f8103ff7cc40a36392ed12a4f38c25f45d71e50e2695eb4acebd670f2f","observation_id":"c454fc5b-04ea-45b8-9b8d-51743f6730fb","resolution":{"observed_at":"2026-05-13T13:52:51.766093Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T07:16:04.687562Z","title":"Learning transferable visual models from natural language supervision","venue":null,"work_id":"ad3e05b3-af3a-4fa2-ab30-c45f9f403277","year":2021},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:7e1e4036dd78d7ee7c0869197f59a1de5ae2edd513e73ba7613098c14d891226","observation_id":"3c7566cf-b74d-4ecc-b0f6-8fe3fdcf0d0a","resolution":{"observed_at":"2026-05-13T13:52:51.751949Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Xnor-net: Imagenet classification using binary convolutional neural networks","venue":null,"work_id":"4b1e1574-d1fa-4bd4-bee8-2d74159b8893","year":2016},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:e3715fe567c75291370da54e17f770aad941290ca4dfe6364c24a13e491fc845","observation_id":"b8233e75-fbf3-4ee4-b2d8-008cd2548625","resolution":{"observed_at":"2026-05-13T13:52:51.816447Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"The perceptron: a probabilistic model for information storage and organization in the brain.Psychological review, 65(6):386","venue":null,"work_id":"499e5269-2720-497d-b8c0-ef06dfe5b098","year":1958},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:4a18ff230c80e29c93882ba07327452d452dbb79112a5fa6c4f2e1a5c910d6f9","observation_id":"8c8c5733-290b-4cf3-b834-1c0f126e30c0","resolution":{"observed_at":"2026-05-13T13:52:51.737902Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Conversion of continuous-valued deep networks to efficient event-driven networks for image classification.Frontiers in neuroscience, 11:682","venue":null,"work_id":"47e5c45b-3c86-4fc4-853c-b2b1de8f29d1","year":2017},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:db8882e6abbb48bd53bf9df4b2f06d5af1a426f066f5625e270fc76f2cc6cd23","observation_id":"f953e760-a396-487f-b4a8-42b4472e34fb","resolution":{"observed_at":"2026-05-13T13:52:51.664543Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T15:56:19.744764Z","title":"Learning representations by back-propagating errors.nature, 323(6088):533–536","venue":null,"work_id":"ec5289ca-1c80-4dca-92de-daaf18d78d17","year":1986},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:664ccd13f9246ed53c101fa043b5f3c1e0f326dcef758f565f48d1fff0c063b9","observation_id":"2c73ecd5-7368-4750-bcf8-1659d33dcf63","resolution":{"observed_at":"2026-05-13T13:52:51.798973Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T17:05:09.037434Z","title":"Winogrande: An adversarial winograd schema challenge at scale.Communications of the ACM, 64(9):99–106","venue":null,"work_id":"2a37a93b-1467-4ea5-94be-d5ec6f87efc3","year":2021},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:16d51c98600f10525804cbfaa9811e55cc836b5c9db7a52e6f69a0768d52fe56","observation_id":"c940d5fd-e200-4d30-8a34-c66ab9b991c1","resolution":{"observed_at":"2026-05-13T13:52:51.625852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.00695","last_updated":"2019-08-02T04:01:13Z","snapshot_observed_at":"2026-08-12T14:46:00.654058Z","submitted_at":"2019-08-02T04:01:13Z","title":"Deep ReLU network approximation of functions on a manifold","version":1},"cited_work":{"arxiv_id":"1908.00695","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1908.00695","snapshot_observed_at":"2026-07-02T23:57:28.105803Z","title":"Deep relu network approximation of functions on a manifold.arXiv preprint arXiv:1908.00695","venue":null,"work_id":"44486f6f-2c4c-485b-a6ba-8d4e8fb19e17","year":1908},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"cited_paper":"/paper/1908.00695","citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:8460cd7b83721c3a10e25ef99372f43cc7e8cd344cd00f55671bf75ebf345647","observation_id":"4b20b56c-8174-46a5-98e9-f511c38c33f1","resolution":{"observed_at":"2026-05-13T02:07:08.000306Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Nonparametric regression using deep neural networks with ReLU activation function.The Annals of Statistics, 48(4):1875 – 1897","venue":null,"work_id":"ed971058-1f5e-4a4d-abc9-3f716d0bf175","year":2020},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:f2f29f2af05545eea41551716499adba000da221244662895f2749389f757ae9","observation_id":"97a59e67-3e05-4131-942f-949bebcf89b9","resolution":{"observed_at":"2026-05-13T13:52:51.593068Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Going deeper in spiking neural networks: Vgg and residual architectures.Frontiers in neuroscience, 13:95","venue":null,"work_id":"8b7b06ce-94bd-4ab1-8a34-e0649a5e521e","year":2019},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:c638a7c5246a73974236437f3c99c6aa4f50a41e5760f73bc26eb3e11c76bc5f","observation_id":"8283b32e-af10-45de-9822-5403f9241471","resolution":{"observed_at":"2026-05-13T13:52:51.802917Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.05202","last_updated":"2020-02-12T19:57:13Z","snapshot_observed_at":"2026-08-11T06:21:56.129166Z","submitted_at":"2020-02-12T19:57:13Z","title":"GLU Variants Improve Transformer","version":1},"cited_work":{"arxiv_id":"2002.05202","doi":"10.48550/arxiv.2002.05202","metadata_source":"pith","pith_arxiv_id":"2002.05202","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"GLU Variants Improve Transformer","venue":"cs.LG","work_id":"17d0763c-1016-41ab-a478-478e890765eb","year":2020},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"cited_paper":"/paper/2002.05202","citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:a51599b905cfdb7b148acc2605b217256af5ad44f9b1e28c4ac13946c970d6a4","observation_id":"ebe3a1f6-db0f-423d-9a77-8c62780caed3","resolution":{"observed_at":"2026-05-13T02:07:08.022000Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-07-13T15:50:07.002485+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-13T15:50:07.002485+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03267","last_updated":"2026-05-01T23:55:43Z","snapshot_observed_at":"2026-08-02T10:52:10.211700Z","submitted_at":"2025-12-19T07:05:38Z","title":"OpenAI GPT-5 System Card","version":2},"cited_work":{"arxiv_id":"2601.03267","doi":"10.48550/arxiv.2601.03267","metadata_source":"pith","pith_arxiv_id":"2601.03267","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"OpenAI GPT-5 System Card","venue":"cs.CL","work_id":"ca87689a-0d29-4476-b504-b65dbbb08af4","year":2025},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"cited_paper":"/paper/2601.03267","citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:f5613eda46967e1786b95210610992fbf65bb5992a61b16fe30cc398838c5490","observation_id":"7c35b50a-c6da-47ce-8e94-002943a02c22","resolution":{"observed_at":"2026-05-13T02:07:07.976497Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-10T00:38:24.809631+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-10T00:38:24.809631+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Low curvature activations reduce overfitting in adversarial training","venue":null,"work_id":"167a7a1b-20ff-4575-8e57-91ebe75e4ba4","year":2021},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:2aaa088a7c2504308b7d65238308915ce5d7641dec039ad1e99e9b405e18905e","observation_id":"8f971808-a027-43eb-bd43-e8db25f8fa99","resolution":{"observed_at":"2026-05-13T13:52:51.606524Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:45:00.553668Z","title":"Deep learning in spiking neural networks.Neural networks, 111:47–63","venue":null,"work_id":"2e5e36a8-8a83-4edc-97ec-af15e901166e","year":2019},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:589fb2aad66dec1be3eed96cbf50bf9af62c647415de46cd12efc981a3b1530c","observation_id":"efd38078-e91d-4d3e-ab3d-f41b621c48f4","resolution":{"observed_at":"2026-05-13T13:52:51.671704Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.09288","last_updated":"2023-07-19T17:08:59Z","snapshot_observed_at":"2026-08-07T12:56:43.323460Z","submitted_at":"2023-07-18T14:31:57Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","version":2},"cited_work":{"arxiv_id":"2307.09288","doi":"10.24963/ijcai.2025/706","metadata_source":"pith","pith_arxiv_id":"2307.09288","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","venue":"cs.CL","work_id":"68a5177f-d644-44c1-bd4f-4e5278c22f5d","year":2023},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"cited_paper":"/paper/2307.09288","citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:b85beff01207991f3b85b82f94f002c247d08c5f1faebb6eed93908872f76351","observation_id":"cc080a8a-9723-4f21-afa4-728d508206cf","resolution":{"observed_at":"2026-05-13T02:07:07.945698Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Stochastic concept bottleneck models.Advances in Neural Information Processing Systems, 37:51787–51810","venue":null,"work_id":"777ede2e-bb62-4deb-b875-48b7d9fa6546","year":2024},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:60e5b293401a3a530a4917bb89bc5ff27c703c21ea93fb8aeb960eeabcf2007a","observation_id":"9f21a6ae-3c61-4565-be54-5b246532b214","resolution":{"observed_at":"2026-05-13T13:52:51.597675Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"e8ac7a1b-4f74-4d1f-9490-a8e650270171","year":2011},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:4bbc092078f4ab5fedaf009c7708b8d58e74668c6a2ce17f8a0ae89e69cb5497","observation_id":"3c236e00-a969-485c-8dd4-8b5cb97d02af","resolution":{"observed_at":"2026-05-13T13:52:51.616490Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.11453","last_updated":"2023-10-17T17:59:15Z","snapshot_observed_at":"2026-08-11T03:44:43.109946Z","submitted_at":"2023-10-17T17:59:15Z","title":"BitNet: Scaling 1-bit Transformers for Large Language Models","version":1},"cited_work":{"arxiv_id":"2310.11453","doi":"10.48550/arxiv.2310.11453","metadata_source":"pith","pith_arxiv_id":"2310.11453","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"BitNet: Scaling 1-bit Transformers for Large Language Models","venue":"cs.CL","work_id":"28ad8f61-4291-4894-b120-1d42fc9937a3","year":2023},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"cited_paper":"/paper/2310.11453","citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:a3da144bdf0e285bb6f66c7cf17647a363ed80aa64a31cd8aa488ddcf1cff1b3","observation_id":"e706d0d4-37fd-4cc7-a7a2-e61f43a52145","resolution":{"observed_at":"2026-05-13T02:07:07.901449Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.00297","last_updated":"2018-07-01T09:01:52Z","snapshot_observed_at":"2026-08-04T20:18:10.091077Z","submitted_at":"2018-07-01T09:01:52Z","title":"Exponential Convergence of the Deep Neural Network Approximation for Analytic Functions","version":1},"cited_work":{"arxiv_id":"1807.00297","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1807.00297","snapshot_observed_at":"2026-07-04T22:52:49.458199Z","title":"Exponential convergence of the deep neural network approximation for analytic functions","venue":null,"work_id":"5e37e98b-05da-473f-b6f0-ffffe1bc27e5","year":2018},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"cited_paper":"/paper/1807.00297","citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:d6f4890502683d5a0bbf3b23cc6e23d571bb280afe47bf3eb1b66ecee9647249","observation_id":"c7256990-480f-46e4-a193-d94bf6241877","resolution":{"observed_at":"2026-07-04T22:52:49.458199Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.24432/c55c7w","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Warwick Nash, Tracy Sellers, Simon Talbot, Andrew Cawthorn, and Wes Ford","venue":"California Digital Library","work_id":"327f1c82-2673-44eb-91be-6d23c2e9149a","year":1994},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:d2860cbbb7b30dd4622a426a8e5e95bf85729e625d70f53a6bb988c983723ab0","observation_id":"7c89109b-cd45-49d3-b3e0-152f4e9a567e","resolution":{"observed_at":"2026-05-13T02:07:07.279976Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-03T15:09:15.893961+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-03T15:09:15.893961+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.06201","last_updated":"2015-11-19T15:14:02Z","snapshot_observed_at":"2026-07-06T04:37:04.029603Z","submitted_at":"2015-11-19T15:14:02Z","title":"Adjustable Bounded Rectifiers: Towards Deep Binary Representations","version":1},"cited_work":{"arxiv_id":"1511.06201","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1511.06201","snapshot_observed_at":"2026-07-04T20:41:08.077766Z","title":"Adjustable bounded rectifiers: Towards deep binary representations","venue":null,"work_id":"ddb8be19-eef6-4f7b-99a3-a90409d44f43","year":2015},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"cited_paper":"/paper/1511.06201","citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:ea16146dc13371c7c2dc86d7e37fb1f1181225e5d7780e12a7779a16d17fdaec","observation_id":"7f3ce70c-22cc-4bc5-84e0-6d76e9e13bfc","resolution":{"observed_at":"2026-07-04T20:41:08.077766Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.14536","last_updated":"2021-07-11T00:56:58Z","snapshot_observed_at":"2026-08-05T15:02:36.455692Z","submitted_at":"2020-06-25T16:34:39Z","title":"Smooth Adversarial Training","version":2},"cited_work":{"arxiv_id":"2006.14536","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.14536","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Smooth Adversarial Training","venue":null,"work_id":"8ca9ec97-43f8-4a04-822c-0a2c22bb2c64","year":2006},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"cited_paper":"/paper/2006.14536","citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:da36871d195bc13ac1e73be0c842e70a61313cd078fbfa3b77f8e56379363736","observation_id":"5f7d27c0-5fc7-4d29-9214-aba618109788","resolution":{"observed_at":"2026-05-13T02:07:07.882428Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Optimal rates of approximation by shallow ReLUk neural networks and applications to nonparametric regression.Constructive Approximation, pages 1–32","venue":null,"work_id":"49bfbe2b-5a21-4d28-87db-273841963446","year":2024},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:2ae1867a4d4cdc3e6ffdab06da8d49e6a3baca994a85bfe4bfe212747b6f369d","observation_id":"cce9c5aa-dc19-4364-a6a8-000457ab6b68","resolution":{"observed_at":"2026-05-13T13:52:51.643639Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T14:37:16.141088Z","title":"Error bounds for approximations with deep ReLU networks.Neural Networks, 94:103–114","venue":null,"work_id":"d8b89680-c9f9-46a1-83fa-04b932368dbe","year":2017},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:295451af5423dc68dc05c6a193644b19979c50d06da649b33029fa4385778cac","observation_id":"705487da-dc30-4aa9-ab11-cb2fa7156b45","resolution":{"observed_at":"2026-05-13T13:52:51.583779Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.05662","last_updated":"2019-09-25T14:33:44Z","snapshot_observed_at":"2026-07-06T07:39:06.044012Z","submitted_at":"2019-03-13T18:23:43Z","title":"Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets","version":4},"cited_work":{"arxiv_id":"1903.05662","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1903.05662","snapshot_observed_at":"2026-07-04T16:59:58.427669Z","title":"Understanding straight-through estimator in training activation quantized neural nets","venue":null,"work_id":"88958bb5-572d-40be-8cc7-9a5173285339","year":1903},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"cited_paper":"/paper/1903.05662","citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:662e767cd3aec2e58ed04429d0a45f7785afcb77f941031816cd6151b8373fb6","observation_id":"2a54ecb4-049a-4e40-957a-a760461bc804","resolution":{"observed_at":"2026-05-13T02:07:07.920946Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Torchcv: A pytorch-based framework for deep learning in computer vision.https://github.com/donnyyou/torchcv","venue":null,"work_id":"73d049c5-a57f-4e94-8d88-4df802e14de3","year":2019},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:3a2530d06b6510083bcda16e0249e6c9eac7c541a55ef605ced84946da8f8e0a","observation_id":"56015d91-db34-48aa-b16f-13c683275b36","resolution":{"observed_at":"2026-05-13T13:52:51.746915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning interpretable differentiable logic networks.IEEE Transactions on Circuits and Systems for Artificial Intelligence","venue":null,"work_id":"903788b6-5d0b-4a6f-a8d5-9221dce76306","year":2024},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:5d3eb24634f368580e8188369d4fd3d153ad1964354a9ae25b5c6eed6e9ef1c8","observation_id":"91c71328-3c29-4fc1-a25f-61734a0d5b65","resolution":{"observed_at":"2026-05-13T13:52:51.630291Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.01936","last_updated":"2023-03-23T23:21:38Z","snapshot_observed_at":"2026-08-13T14:13:01.385510Z","submitted_at":"2022-10-04T22:13:25Z","title":"When and why vision-language models behave like bags-of-words, and what to do about it?","version":3},"cited_work":{"arxiv_id":"2210.01936","doi":null,"metadata_source":"pith","pith_arxiv_id":"2210.01936","snapshot_observed_at":"2026-07-09T19:36:29.281490Z","title":"International Conference on Learning Representations (ICLR) , year =","venue":"cs.CV","work_id":"f61f18f6-7b0d-4a42-91d7-f7dc1efcd5bf","year":2022},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"cited_paper":"/paper/2210.01936","citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:d6a40c4407ecdc8cb1794fdbff63d4b8a33e67aac54ab0e8890451b3637b8065","observation_id":"29fa1dec-d6fb-4b8e-a0f3-2a0850ee0988","resolution":{"observed_at":"2026-05-13T02:07:07.934212Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.15480","last_updated":"2023-02-01T22:26:15Z","snapshot_observed_at":"2026-08-13T15:33:39.594905Z","submitted_at":"2022-05-31T00:29:26Z","title":"Post-hoc Concept Bottleneck Models","version":2},"cited_work":{"arxiv_id":"2205.15480","doi":"10.48550/arxiv.2205.15480","metadata_source":"arxiv_reference","pith_arxiv_id":"2205.15480","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"arXiv preprint arXiv:2205.15480 , year=","venue":"arXiv (Cornell University)","work_id":"ad7c820b-fdef-4c7b-b65c-05ef0c907265","year":2022},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"cited_paper":"/paper/2205.15480","citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:6cc95f128a0980a1e382d843c67a26bb2eabdcec9650bb4b34e202b7e231b20f","observation_id":"39712e7f-ecb7-4cc9-a1eb-38be191750a5","resolution":{"observed_at":"2026-05-13T02:07:08.006383Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1605.07146","last_updated":"2017-06-14T06:06:48Z","snapshot_observed_at":"2026-08-13T10:21:59.687060Z","submitted_at":"2016-05-23T19:27:13Z","title":"Wide Residual Networks","version":4},"cited_work":{"arxiv_id":"1605.07146","doi":null,"metadata_source":"pith","pith_arxiv_id":"1605.07146","snapshot_observed_at":"2026-07-10T12:07:03.509352Z","title":"Wide Residual Networks","venue":"cs.CV","work_id":"1b918c80-6bca-4d06-8019-569626fb1cf2","year":2016},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"cited_paper":"/paper/1605.07146","citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:c9328b2137e91d1acf14a9417ed226dd16abf85e8c47277d00afe9fd14cdc139","observation_id":"721da4f0-c912-4678-90ff-ed877d3fc919","resolution":{"observed_at":"2026-05-13T02:07:08.011670Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Superspike: Supervised learning in multilayer spiking neural networks.Neural computation, 30(6):1514–1541","venue":null,"work_id":"e83e8b7e-49b8-47b1-9d88-05b6bd361131","year":2018},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:ee3a5bc7fefc166774f929f0e0e0fe56171b3a37d46fc902f1806a958e71c449","observation_id":"4fd1393b-8a85-49ac-a8c7-6bb2eb39e088","resolution":{"observed_at":"2026-05-13T13:52:51.611965Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"light-duty","venue":null,"work_id":"1ffa17cc-f910-4d99-bc5e-46f89c8e540d","year":2024},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:8bb582978bff1a1e24691f2c9de90bc19aa158e56215901160f9bbdcf295012d","observation_id":"6a07faae-4aea-418d-8007-1ef98c181e24","resolution":{"observed_at":"2026-05-13T13:52:51.647772Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations"},"reference_resolution":{"displayed":84,"state_counts":{"malformed_identifier":1,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":1,"verified_exact":24,"verified_fuzzy":56},"total_outbound_references":84},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 84 of 84 outbound references and 0 inbound Pith citation observations for arXiv:2605.11558."}