{"as_of":"2026-08-20T11:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:75c8dc77f767382b8bde5208f560a2dccf8634744d799d885a96821c28835f7d","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":24,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":24,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":24,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":24,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T11:34:26.650469Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-10T06:15:00.866473Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1905.02244","last_updated":"2019-11-20T17:26:40Z","snapshot_observed_at":"2026-08-17T11:57:08.807566Z","submitted_at":"2019-05-06T19:38:31Z","title":"Searching for MobileNetV3","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.02244","snapshot_observed_at":"2026-08-14T15:24:16.487500Z","title":"CoRR abs/1905.02244 (2019), http://arxiv.org/abs/1905.02244 8","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"1908.01259","last_updated":"2021-03-25T17:16:13Z","snapshot_observed_at":"2026-08-19T14:13:11.993886Z","submitted_at":"2019-08-04T02:17:34Z","title":"Attentive Normalization","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T15:24:16.487500Z"},"links":{"cited_paper":"/paper/1905.02244","citing_paper":"/paper/1908.01259"},"observation_digest":"sha256:1793bea112b52b4ead42edda977bad006c2f47000a2cc5532473677420c700f7","observation_id":"73301ef8-ec96-4b58-b85c-19999e4f4a49","resolution":{"observed_at":"2026-08-14T15:24:16.487500Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.02244","last_updated":"2019-11-20T17:26:40Z","snapshot_observed_at":"2026-08-17T11:57:08.807566Z","submitted_at":"2019-05-06T19:38:31Z","title":"Searching for MobileNetV3","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.02244","snapshot_observed_at":"2026-08-14T15:20:48.342632Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"1908.01314","last_updated":"2020-03-03T04:11:41Z","snapshot_observed_at":"2026-08-19T13:44:09.789168Z","submitted_at":"2019-08-04T10:40:04Z","title":"MoGA: Searching Beyond MobileNetV3","version":4},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-14T15:20:48.342632Z"},"links":{"cited_paper":"/paper/1905.02244","citing_paper":"/paper/1908.01314"},"observation_digest":"sha256:bbfbd961f64794e1eaca16472c7b5aa6cb7781ef0153cd1e21a5283f004efd56","observation_id":"dccaf974-b7d0-4528-821f-8b79ee72f334","resolution":{"observed_at":"2026-08-14T15:20:48.342632Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.02244","last_updated":"2019-11-20T17:26:40Z","snapshot_observed_at":"2026-08-17T11:57:08.807566Z","submitted_at":"2019-05-06T19:38:31Z","title":"Searching for MobileNetV3","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.02244","snapshot_observed_at":"2026-08-14T15:09:18.157346Z","title":"Searching for MobileNetV3,","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"1908.01748","last_updated":"2019-08-08T06:46:25Z","snapshot_observed_at":"2026-08-18T20:50:15.468334Z","submitted_at":"2019-08-05T17:46:36Z","title":"SqueezeNAS: Fast neural architecture search for faster semantic segmentation","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:18.157346Z"},"links":{"cited_paper":"/paper/1905.02244","citing_paper":"/paper/1908.01748"},"observation_digest":"sha256:ac1da13efd5ceb90610a85f7697dbbe52fb5fa83881308d7a908d53699da8ace","observation_id":"0babaddc-428d-4d6e-bbb9-31e9872d4dcc","resolution":{"observed_at":"2026-08-14T15:09:18.157346Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.02244","last_updated":"2019-11-20T17:26:40Z","snapshot_observed_at":"2026-08-17T11:57:08.807566Z","submitted_at":"2019-05-06T19:38:31Z","title":"Searching for MobileNetV3","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.02244","snapshot_observed_at":"2026-08-14T12:19:53.521579Z","title":"Searching for mobilenetv3","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"1908.08926","last_updated":"2019-08-20T23:26:04Z","snapshot_observed_at":"2026-08-17T10:58:57.184484Z","submitted_at":"2019-08-20T23:26:04Z","title":"Efficient Deep Neural Networks","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-14T12:19:53.521579Z"},"links":{"cited_paper":"/paper/1905.02244","citing_paper":"/paper/1908.08926"},"observation_digest":"sha256:73de1e39740c475735eb98c77f349780c2bf16ffd74e4d79cbac34bbe7511dd3","observation_id":"611672a4-baa9-43a5-b5dc-2a6af153f9c1","resolution":{"observed_at":"2026-08-14T12:19:53.521579Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.02244","last_updated":"2019-11-20T17:26:40Z","snapshot_observed_at":"2026-08-17T11:57:08.807566Z","submitted_at":"2019-05-06T19:38:31Z","title":"Searching for MobileNetV3","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.02244","snapshot_observed_at":"2026-08-14T11:25:00.969061Z","title":"Searching for mobilenetv3","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"1908.09124","last_updated":"2019-12-01T12:52:26Z","snapshot_observed_at":"2026-08-17T11:56:54.432275Z","submitted_at":"2019-08-24T11:21:38Z","title":"SeesawFaceNets: sparse and robust face verification model for mobile platform","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T11:25:00.969061Z"},"links":{"cited_paper":"/paper/1905.02244","citing_paper":"/paper/1908.09124"},"observation_digest":"sha256:69fc27ed0734bd82debfa58672e667d9c6ccd63c1812625880fc030cbf0e9928","observation_id":"acd7a821-b93e-49e2-ba59-22eae6049d65","resolution":{"observed_at":"2026-08-14T11:25:00.969061Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.02244","last_updated":"2019-11-20T17:26:40Z","snapshot_observed_at":"2026-08-17T11:57:08.807566Z","submitted_at":"2019-05-06T19:38:31Z","title":"Searching for MobileNetV3","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.02244","snapshot_observed_at":"2026-08-14T11:02:29.347682Z","title":"Searching for mobilenetv3","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"1908.10030","last_updated":"2019-08-27T04:49:23Z","snapshot_observed_at":"2026-08-19T23:07:26.034139Z","submitted_at":"2019-08-27T04:49:23Z","title":"Finite size corrections for neural network Gaussian processes","version":1},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-14T11:02:29.347682Z"},"links":{"cited_paper":"/paper/1905.02244","citing_paper":"/paper/1908.10030"},"observation_digest":"sha256:e71a34e0892e743379c0318e780e4d2652a47360c0534e2b327b08de2539c337","observation_id":"d2b6b1d6-6883-4232-ac4a-eaa08b932dd5","resolution":{"observed_at":"2026-08-14T11:02:29.347682Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.02244","last_updated":"2019-11-20T17:26:40Z","snapshot_observed_at":"2026-08-17T11:57:08.807566Z","submitted_at":"2019-05-06T19:38:31Z","title":"Searching for MobileNetV3","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.02244","snapshot_observed_at":"2026-08-12T10:34:38.318218Z","title":"Le, and Hartwig Adam","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2411.19114","last_updated":"2024-11-28T13:02:41Z","snapshot_observed_at":"2026-08-17T11:56:24.937933Z","submitted_at":"2024-11-28T13:02:41Z","title":"PREBA: A Hardware/Software Co-Design for Multi-Instance GPU based AI Inference Servers","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T10:34:38.318218Z"},"links":{"cited_paper":"/paper/1905.02244","citing_paper":"/paper/2411.19114"},"observation_digest":"sha256:be3fc33f68446ecda5f7c85dc695ffd526da1957ebff63d183bc0a6899afa90e","observation_id":"cf5c742e-d1f2-4ad5-bf44-4c93cb03ceb8","resolution":{"observed_at":"2026-08-12T10:34:38.318218Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.02244","last_updated":"2019-11-20T17:26:40Z","snapshot_observed_at":"2026-08-17T11:57:08.807566Z","submitted_at":"2019-05-06T19:38:31Z","title":"Searching for MobileNetV3","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.02244","snapshot_observed_at":"2026-08-12T04:21:08.743999Z","title":"Le, and Hartwig Adam","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2412.01508","last_updated":"2024-12-02T14:01:44Z","snapshot_observed_at":"2026-08-17T21:57:36.386756Z","submitted_at":"2024-12-02T14:01:44Z","title":"HaGRIDv2: 1M Images for Static and Dynamic Hand Gesture Recognition","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:08.743999Z"},"links":{"cited_paper":"/paper/1905.02244","citing_paper":"/paper/2412.01508"},"observation_digest":"sha256:f44b0d3d8f15cfb6f048d1dc594339653486d6c0156f61a14b67cc91d9f1547b","observation_id":"4e5a6bed-2a60-4703-aa6c-859d8250208f","resolution":{"observed_at":"2026-08-12T04:21:08.743999Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.02244","last_updated":"2019-11-20T17:26:40Z","snapshot_observed_at":"2026-08-17T11:57:08.807566Z","submitted_at":"2019-05-06T19:38:31Z","title":"Searching for MobileNetV3","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.02244","snapshot_observed_at":"2026-08-11T15:00:06.904611Z","title":"Searching for mobilenetv3,","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2412.11452","last_updated":"2024-12-16T05:14:08Z","snapshot_observed_at":"2026-08-20T01:42:47.818036Z","submitted_at":"2024-12-16T05:14:08Z","title":"Multilabel Classification for Lung Disease Detection: Integrating Deep Learning and Natural Language Processing","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:06.904611Z"},"links":{"cited_paper":"/paper/1905.02244","citing_paper":"/paper/2412.11452"},"observation_digest":"sha256:cf113cd193731493ecc15c73c25bee236854a3c1ede2364373b925cef59b4aae","observation_id":"765e4bc3-3d69-4d81-85f0-10899a380e97","resolution":{"observed_at":"2026-08-11T15:00:06.904611Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.02244","last_updated":"2019-11-20T17:26:40Z","snapshot_observed_at":"2026-08-17T11:57:08.807566Z","submitted_at":"2019-05-06T19:38:31Z","title":"Searching for MobileNetV3","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.02244","snapshot_observed_at":"2026-08-10T17:30:21.363068Z","title":"V., and Adam, H","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.12191","last_updated":"2026-06-20T15:49:55Z","snapshot_observed_at":"2026-08-14T06:52:39.967260Z","submitted_at":"2025-01-21T14:56:47Z","title":"HEM: a margin-based loss for visual categorisation tasks","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-10T17:30:21.363068Z"},"links":{"cited_paper":"/paper/1905.02244","citing_paper":"/paper/2501.12191"},"observation_digest":"sha256:03051ca8b1c3413145fca4a1cedead58b29578cfc3885fb3cb65f433c5d5f649","observation_id":"5b3c837e-cff6-4f99-9e2d-2d0f35132eaa","resolution":{"observed_at":"2026-08-10T17:30:21.363068Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.02244","last_updated":"2019-11-20T17:26:40Z","snapshot_observed_at":"2026-08-17T11:57:08.807566Z","submitted_at":"2019-05-06T19:38:31Z","title":"Searching for MobileNetV3","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.02244","snapshot_observed_at":"2026-08-10T14:25:11.939684Z","title":"Searching for mobilenetv3,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.16389","last_updated":"2025-09-07T01:22:45Z","snapshot_observed_at":"2026-08-15T14:51:30.448066Z","submitted_at":"2025-01-26T00:27:04Z","title":"Bridging the Sim2Real Gap: Vision Encoder Pre-Training for Visuomotor Policy Transfer","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T14:25:11.939684Z"},"links":{"cited_paper":"/paper/1905.02244","citing_paper":"/paper/2501.16389"},"observation_digest":"sha256:11af53b16cc73b572da2085ca696e01851913423ca3a65ea046b30a50e7c5d6c","observation_id":"2b7a98e4-f64e-401d-b6eb-70265a5e9452","resolution":{"observed_at":"2026-08-10T14:25:11.939684Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.02244","last_updated":"2019-11-20T17:26:40Z","snapshot_observed_at":"2026-08-17T11:57:08.807566Z","submitted_at":"2019-05-06T19:38:31Z","title":"Searching for MobileNetV3","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.02244","snapshot_observed_at":"2026-08-09T15:49:53.465874Z","title":"Le, and Hartwig Adam","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2502.01303","last_updated":"2025-02-03T12:26:55Z","snapshot_observed_at":"2026-08-17T09:59:48.618465Z","submitted_at":"2025-02-03T12:26:55Z","title":"Partial Channel Network: Compute Fewer, Perform Better","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-09T15:49:53.465874Z"},"links":{"cited_paper":"/paper/1905.02244","citing_paper":"/paper/2502.01303"},"observation_digest":"sha256:ef9bdcd0df47c3f23fdae75358a0a51d4a2d80bac5126803a87ea2bcd9ff275e","observation_id":"67b909b2-b403-4775-bafd-5be0979d2968","resolution":{"observed_at":"2026-08-09T15:49:53.465874Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.02244","last_updated":"2019-11-20T17:26:40Z","snapshot_observed_at":"2026-08-17T11:57:08.807566Z","submitted_at":"2019-05-06T19:38:31Z","title":"Searching for MobileNetV3","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.02244","snapshot_observed_at":"2026-08-16T11:34:26.650469Z","title":"Searching for mobilenetv3,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2504.15185","last_updated":"2025-04-21T15:49:27Z","snapshot_observed_at":"2026-08-19T06:17:00.856566Z","submitted_at":"2025-04-21T15:49:27Z","title":"ForgeBench: A Machine Learning Benchmark Suite and Auto-Generation Framework for Next-Generation HLS Tools","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T11:34:26.650469Z"},"links":{"cited_paper":"/paper/1905.02244","citing_paper":"/paper/2504.15185"},"observation_digest":"sha256:f0f03701b2ea7ff3735cb7926fb231ddefb6f0ea61ec149ae0e652b01163aa30","observation_id":"f24dbd40-6696-4d10-9e6d-2364b4492660","resolution":{"observed_at":"2026-08-16T11:34:26.650469Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.02244","last_updated":"2019-11-20T17:26:40Z","snapshot_observed_at":"2026-08-17T11:57:08.807566Z","submitted_at":"2019-05-06T19:38:31Z","title":"Searching for MobileNetV3","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.02244","snapshot_observed_at":"2026-08-07T15:11:31.147926Z","title":"Howard et al., ”Searching for MobileNetV3,” arXiv preprint arXiv:1905.02244, May 2019","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2506.11049","last_updated":"2025-08-14T14:50:40Z","snapshot_observed_at":"2026-08-19T14:05:22.236325Z","submitted_at":"2025-05-21T21:53:19Z","title":"15,500 Seconds: Lean UAV Classification Using EfficientNet and Lightweight Fine-Tuning","version":4},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:31.147926Z"},"links":{"cited_paper":"/paper/1905.02244","citing_paper":"/paper/2506.11049"},"observation_digest":"sha256:8b47aa8dad0c16deb37713d4724cb8f1b59f09476397b9316597680f3532f585","observation_id":"a3b91a24-7c80-4333-80a8-f0c779975e3e","resolution":{"observed_at":"2026-08-07T15:11:31.147926Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.02244","last_updated":"2019-11-20T17:26:40Z","snapshot_observed_at":"2026-08-17T11:57:08.807566Z","submitted_at":"2019-05-06T19:38:31Z","title":"Searching for MobileNetV3","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.02244","snapshot_observed_at":"2026-08-07T00:32:57.625491Z","title":"Searching for MobileNetV3,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.14846","last_updated":"2025-06-16T15:15:30Z","snapshot_observed_at":"2026-08-17T18:00:25.283096Z","submitted_at":"2025-06-16T15:15:30Z","title":"Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T00:32:57.625491Z"},"links":{"cited_paper":"/paper/1905.02244","citing_paper":"/paper/2506.14846"},"observation_digest":"sha256:b2af9e3a68fd33e456e019b4706ab6c60ddb83350917b76455d64a572e5f3001","observation_id":"3de8894d-8543-4642-882d-69b231e0e88d","resolution":{"observed_at":"2026-08-07T00:32:57.625491Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.02244","last_updated":"2019-11-20T17:26:40Z","snapshot_observed_at":"2026-08-17T11:57:08.807566Z","submitted_at":"2019-05-06T19:38:31Z","title":"Searching for MobileNetV3","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.02244","snapshot_observed_at":"2026-08-06T19:03:12.515597Z","title":"Searching for MobileNetV3,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.06687","last_updated":"2025-07-09T09:30:07Z","snapshot_observed_at":"2026-08-16T11:40:23.318807Z","submitted_at":"2025-07-09T09:30:07Z","title":"StixelNExT++: Lightweight Monocular Scene Segmentation and Representation for Collective Perception","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T19:03:12.515597Z"},"links":{"cited_paper":"/paper/1905.02244","citing_paper":"/paper/2507.06687"},"observation_digest":"sha256:eafb0086f67db289d3899d2d940e5de1b2996ae518e9fc09dd0fe2af7793a3a3","observation_id":"220cab35-4ea2-4dbe-8b98-5ad572a68761","resolution":{"observed_at":"2026-08-06T19:03:12.515597Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.02244","last_updated":"2019-11-20T17:26:40Z","snapshot_observed_at":"2026-08-17T11:57:08.807566Z","submitted_at":"2019-05-06T19:38:31Z","title":"Searching for MobileNetV3","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.02244","snapshot_observed_at":"2026-08-06T17:58:37.032239Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.14185","last_updated":"2025-07-13T02:58:48Z","snapshot_observed_at":"2026-08-20T07:29:10.683356Z","submitted_at":"2025-07-13T02:58:48Z","title":"Latent Sensor Fusion: Multimedia Learning of Physiological Signals for Resource-Constrained Devices","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T17:58:37.032239Z"},"links":{"cited_paper":"/paper/1905.02244","citing_paper":"/paper/2507.14185"},"observation_digest":"sha256:12f5ba88fec86b10e707653055e43f7921849fc57092d4b4941a55bafb4b502e","observation_id":"e3ad1e7e-f10e-470d-b364-45e5f002f158","resolution":{"observed_at":"2026-08-06T17:58:37.032239Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.02244","last_updated":"2019-11-20T17:26:40Z","snapshot_observed_at":"2026-08-17T11:57:08.807566Z","submitted_at":"2019-05-06T19:38:31Z","title":"Searching for MobileNetV3","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.02244","snapshot_observed_at":"2026-08-06T11:56:59.792872Z","title":"Le, and Hartwig Adam","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-18T09:18:46.778146Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.792872Z"},"links":{"cited_paper":"/paper/1905.02244","citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:244010f5e7e81904a67605881fe967cb212f875cf5d68948b81b9554db5f0378","observation_id":"6e01ed75-1705-477f-8b6b-a0a776a1311f","resolution":{"observed_at":"2026-08-06T11:56:59.792872Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.02244","last_updated":"2019-11-20T17:26:40Z","snapshot_observed_at":"2026-08-17T11:57:08.807566Z","submitted_at":"2019-05-06T19:38:31Z","title":"Searching for MobileNetV3","version":5},"cited_work":{"arxiv_id":"1905.02244","doi":"10.48550/arxiv.1905.02244","metadata_source":"arxiv_reference","pith_arxiv_id":"1905.02244","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"author Sandler, M","venue":null,"work_id":"d934d8a0-902e-41cc-8705-0b216034418a","year":2019},"citing_paper":{"arxiv_id":"2604.17476","last_updated":"2026-04-19T15:07:01Z","snapshot_observed_at":"2026-07-06T23:04:32.734175Z","submitted_at":"2026-04-19T15:07:01Z","title":"Privatar: Scalable Privacy-preserving Multi-user VR via Secure Offloading","version":1},"reference_index":125,"source":"arxiv_source","source_observed_at":"2026-05-10T06:20:18.479234Z"},"links":{"cited_paper":"/paper/1905.02244","citing_paper":"/paper/2604.17476"},"observation_digest":"sha256:7f9913bee44887d37d3bcc563a499e18be957a8a585125829675a9f572baac7e","observation_id":"9a8bd941-eaf8-45d3-b38f-36e37f4ac004","resolution":{"observed_at":"2026-05-10T06:21:26.211383Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.02244","last_updated":"2019-11-20T17:26:40Z","snapshot_observed_at":"2026-08-17T11:57:08.807566Z","submitted_at":"2019-05-06T19:38:31Z","title":"Searching for MobileNetV3","version":5},"cited_work":{"arxiv_id":"1905.02244","doi":"10.48550/arxiv.1905.02244","metadata_source":"arxiv_reference","pith_arxiv_id":"1905.02244","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"author Sandler, M","venue":null,"work_id":"d934d8a0-902e-41cc-8705-0b216034418a","year":2019},"citing_paper":{"arxiv_id":"2605.03039","last_updated":"2026-05-04T18:06:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-05-04T18:06:17Z","title":"Mixed-Precision Information Bottlenecks for On-Device Trait-State Disentanglement in Bipolar Agitation Detection","version":1},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-05-08T19:11:30.638672Z"},"links":{"cited_paper":"/paper/1905.02244","citing_paper":"/paper/2605.03039"},"observation_digest":"sha256:06b3cb11b6d381e5f84ece600492670ffbc80fafa862ddf66c9faa0f6ba959ab","observation_id":"e43211c7-78c2-4a8b-91fe-4f65811c4185","resolution":{"observed_at":"2026-05-09T06:00:36.670372Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.02244","last_updated":"2019-11-20T17:26:40Z","snapshot_observed_at":"2026-08-17T11:57:08.807566Z","submitted_at":"2019-05-06T19:38:31Z","title":"Searching for MobileNetV3","version":5},"cited_work":{"arxiv_id":"1905.02244","doi":"10.48550/arxiv.1905.02244","metadata_source":"arxiv_reference","pith_arxiv_id":"1905.02244","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"author Sandler, M","venue":null,"work_id":"d934d8a0-902e-41cc-8705-0b216034418a","year":2019},"citing_paper":{"arxiv_id":"2605.20250","last_updated":"2026-05-18T08:02:28Z","snapshot_observed_at":"2026-08-15T15:40:27.028915Z","submitted_at":"2026-05-18T08:02:28Z","title":"Physics-informed convolutional neural networks for fluid flow through porous media","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-05-21T08:21:16.460521Z"},"links":{"cited_paper":"/paper/1905.02244","citing_paper":"/paper/2605.20250"},"observation_digest":"sha256:b10e1a70fef81d51662d34615b9991f5248c67e372d8a9a74d392d8ab152da10","observation_id":"bd874c2b-cc73-4be1-aafd-6a3364a6a6df","resolution":{"observed_at":"2026-05-21T08:24:03.508603Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.02244","last_updated":"2019-11-20T17:26:40Z","snapshot_observed_at":"2026-08-17T11:57:08.807566Z","submitted_at":"2019-05-06T19:38:31Z","title":"Searching for MobileNetV3","version":5},"cited_work":{"arxiv_id":"1905.02244","doi":"10.48550/arxiv.1905.02244","metadata_source":"arxiv_reference","pith_arxiv_id":"1905.02244","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"author Sandler, M","venue":null,"work_id":"d934d8a0-902e-41cc-8705-0b216034418a","year":2019},"citing_paper":{"arxiv_id":"2606.02045","last_updated":"2026-06-17T10:32:25Z","snapshot_observed_at":"2026-08-14T22:38:40.741237Z","submitted_at":"2026-06-01T10:36:01Z","title":"Attention mechanisms and transfer learning for robust peach leaf damage classification under domain shift","version":2},"reference_index":110,"source":"arxiv_source","source_observed_at":"2026-06-28T15:36:55.675366Z"},"links":{"cited_paper":"/paper/1905.02244","citing_paper":"/paper/2606.02045"},"observation_digest":"sha256:3a788c25f0c33f06b5cf4baceffdf32b45ed1d2a24ecc27a93bf008a0d4a2957","observation_id":"065a2c9a-2582-4544-b330-8f1024a7b895","resolution":{"observed_at":"2026-07-01T22:16:16.137459Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.02244","last_updated":"2019-11-20T17:26:40Z","snapshot_observed_at":"2026-08-17T11:57:08.807566Z","submitted_at":"2019-05-06T19:38:31Z","title":"Searching for MobileNetV3","version":5},"cited_work":{"arxiv_id":"1905.02244","doi":"10.48550/arxiv.1905.02244","metadata_source":"arxiv_reference","pith_arxiv_id":"1905.02244","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"author Sandler, M","venue":null,"work_id":"d934d8a0-902e-41cc-8705-0b216034418a","year":2019},"citing_paper":{"arxiv_id":"2606.09881","last_updated":"2026-06-03T05:44:29Z","snapshot_observed_at":"2026-08-12T12:37:31.669797Z","submitted_at":"2026-06-03T05:44:29Z","title":"Toward Calibrated, Fair, and accurate Deepfake Detection","version":1},"reference_index":83,"source":"arxiv_source","source_observed_at":"2026-06-28T07:05:18.026601Z"},"links":{"cited_paper":"/paper/1905.02244","citing_paper":"/paper/2606.09881"},"observation_digest":"sha256:3557212694af5913bab532121677ec9c29c12a492895de744604b49f53809f7b","observation_id":"4510ddba-5bd2-4294-b188-0122fc622853","resolution":{"observed_at":"2026-06-28T07:11:45.114433Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.02244","last_updated":"2019-11-20T17:26:40Z","snapshot_observed_at":"2026-08-17T11:57:08.807566Z","submitted_at":"2019-05-06T19:38:31Z","title":"Searching for MobileNetV3","version":5},"cited_work":{"arxiv_id":"1905.02244","doi":"10.48550/arxiv.1905.02244","metadata_source":"arxiv_reference","pith_arxiv_id":"1905.02244","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"author Sandler, M","venue":null,"work_id":"d934d8a0-902e-41cc-8705-0b216034418a","year":2019},"citing_paper":{"arxiv_id":"2606.27579","last_updated":"2026-06-25T22:11:29Z","snapshot_observed_at":"2026-08-17T12:35:30.560028Z","submitted_at":"2026-06-25T22:11:29Z","title":"Distribution-based deep multiple instance learning for tumor proportion scoring in NSCLC","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-29T01:34:38.392583Z"},"links":{"cited_paper":"/paper/1905.02244","citing_paper":"/paper/2606.27579"},"observation_digest":"sha256:84317fd8ece259a985539a714849cfee5ae412d3dd5ff26aeb255c1cfd133ce3","observation_id":"a9b76049-849c-4f87-9b38-c8668b264055","resolution":{"observed_at":"2026-06-29T01:42:58.931504Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1905.02244/citation-record","integrity":"/paper/1905.02244/integrity","json":"/paper/1905.02244/citation-record.json","paper":"/paper/1905.02244"},"outbound":[],"paper":{"arxiv_id":"1905.02244","last_updated":"2019-11-20T17:26:40Z","latest_version":5,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-17T11:57:08.807566Z","submitted_at":"2019-05-06T19:38:31Z","title":"Searching for MobileNetV3"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 24 inbound Pith citation observations for arXiv:1905.02244."}