{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:D7WQIGIFO5HYQVFLCZVCBLNPBQ","short_pith_number":"pith:D7WQIGIF","canonical_record":{"source":{"id":"1612.03480","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-12-11T21:15:05Z","cross_cats_sorted":["cs.NE","q-bio.NC","stat.ML"],"title_canon_sha256":"b93303ab3ed4a460674d186c6abfac29b074066eadda4fe8536d73808f045f2a","abstract_canon_sha256":"9192aa2a40c4aa660bd1443d06661ff9b7a4f45a910cc7f8cffb628cb3aeab52"},"schema_version":"1.0"},"canonical_sha256":"1fed041905774f8854ab166a20adaf0c0124990943718b188bc41e78d3b29565","source":{"kind":"arxiv","id":"1612.03480","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1612.03480","created_at":"2026-05-18T00:48:26Z"},{"alias_kind":"arxiv_version","alias_value":"1612.03480v1","created_at":"2026-05-18T00:48:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.03480","created_at":"2026-05-18T00:48:26Z"},{"alias_kind":"pith_short_12","alias_value":"D7WQIGIFO5HY","created_at":"2026-05-18T12:30:09Z"},{"alias_kind":"pith_short_16","alias_value":"D7WQIGIFO5HYQVFL","created_at":"2026-05-18T12:30:09Z"},{"alias_kind":"pith_short_8","alias_value":"D7WQIGIF","created_at":"2026-05-18T12:30:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:D7WQIGIFO5HYQVFLCZVCBLNPBQ","target":"record","payload":{"canonical_record":{"source":{"id":"1612.03480","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-12-11T21:15:05Z","cross_cats_sorted":["cs.NE","q-bio.NC","stat.ML"],"title_canon_sha256":"b93303ab3ed4a460674d186c6abfac29b074066eadda4fe8536d73808f045f2a","abstract_canon_sha256":"9192aa2a40c4aa660bd1443d06661ff9b7a4f45a910cc7f8cffb628cb3aeab52"},"schema_version":"1.0"},"canonical_sha256":"1fed041905774f8854ab166a20adaf0c0124990943718b188bc41e78d3b29565","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:48:26.034109Z","signature_b64":"6PNCiH2RmZmqj3mYGKCfAqK8n61vWsQWMFFFi5mZ/Rlw9CrvhBNLdxhJ8kJfrb5/cx7KGRAb0hKW4z2ShRz2DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1fed041905774f8854ab166a20adaf0c0124990943718b188bc41e78d3b29565","last_reissued_at":"2026-05-18T00:48:26.033603Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:48:26.033603Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1612.03480","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:48:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6fX4bE2Qa1HxXfYhGn/ZdeFbcqgX5ax1kEMPY+bwWcs+vrSPjTaFA+lQuqpiR+p9awYjtWlVuWchzlUTrQnLCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-22T12:22:22.549455Z"},"content_sha256":"a37b69d520b6e9ef66a7b40ae738d47f2db1f1a3ceae5215f77d44a6c22e9397","schema_version":"1.0","event_id":"sha256:a37b69d520b6e9ef66a7b40ae738d47f2db1f1a3ceae5215f77d44a6c22e9397"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:D7WQIGIFO5HYQVFLCZVCBLNPBQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Self-calibrating Neural Networks for Dimensionality Reduction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE","q-bio.NC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Cengiz Pehlevan, Dmitri B. Chklovskii, Yuansi Chen","submitted_at":"2016-12-11T21:15:05Z","abstract_excerpt":"Recently, a novel family of biologically plausible online algorithms for reducing the dimensionality of streaming data has been derived from the similarity matching principle. In these algorithms, the number of output dimensions can be determined adaptively by thresholding the singular values of the input data matrix. However, setting such threshold requires knowing the magnitude of the desired singular values in advance. Here we propose online algorithms where the threshold is self-calibrating based on the singular values computed from the existing observations. To derive these algorithms fro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.03480","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:48:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ldr7ELhTCLf86ddLCP9r4sWOB7LHzgFm6dI9xIzyPGaUByP5EQ78uVK6OEZWdCpNyXhjc4H/FCDvzpOOT2evAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-22T12:22:22.549808Z"},"content_sha256":"c71158ca81d3f8773ad69e78f9e24b24f5b3aefbdf63f56113f9c4007506fc72","schema_version":"1.0","event_id":"sha256:c71158ca81d3f8773ad69e78f9e24b24f5b3aefbdf63f56113f9c4007506fc72"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/D7WQIGIFO5HYQVFLCZVCBLNPBQ/bundle.json","state_url":"https://pith.science/pith/D7WQIGIFO5HYQVFLCZVCBLNPBQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/D7WQIGIFO5HYQVFLCZVCBLNPBQ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-22T12:22:22Z","links":{"resolver":"https://pith.science/pith/D7WQIGIFO5HYQVFLCZVCBLNPBQ","bundle":"https://pith.science/pith/D7WQIGIFO5HYQVFLCZVCBLNPBQ/bundle.json","state":"https://pith.science/pith/D7WQIGIFO5HYQVFLCZVCBLNPBQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/D7WQIGIFO5HYQVFLCZVCBLNPBQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:D7WQIGIFO5HYQVFLCZVCBLNPBQ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"9192aa2a40c4aa660bd1443d06661ff9b7a4f45a910cc7f8cffb628cb3aeab52","cross_cats_sorted":["cs.NE","q-bio.NC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-12-11T21:15:05Z","title_canon_sha256":"b93303ab3ed4a460674d186c6abfac29b074066eadda4fe8536d73808f045f2a"},"schema_version":"1.0","source":{"id":"1612.03480","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1612.03480","created_at":"2026-05-18T00:48:26Z"},{"alias_kind":"arxiv_version","alias_value":"1612.03480v1","created_at":"2026-05-18T00:48:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.03480","created_at":"2026-05-18T00:48:26Z"},{"alias_kind":"pith_short_12","alias_value":"D7WQIGIFO5HY","created_at":"2026-05-18T12:30:09Z"},{"alias_kind":"pith_short_16","alias_value":"D7WQIGIFO5HYQVFL","created_at":"2026-05-18T12:30:09Z"},{"alias_kind":"pith_short_8","alias_value":"D7WQIGIF","created_at":"2026-05-18T12:30:09Z"}],"graph_snapshots":[{"event_id":"sha256:c71158ca81d3f8773ad69e78f9e24b24f5b3aefbdf63f56113f9c4007506fc72","target":"graph","created_at":"2026-05-18T00:48:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Recently, a novel family of biologically plausible online algorithms for reducing the dimensionality of streaming data has been derived from the similarity matching principle. In these algorithms, the number of output dimensions can be determined adaptively by thresholding the singular values of the input data matrix. However, setting such threshold requires knowing the magnitude of the desired singular values in advance. Here we propose online algorithms where the threshold is self-calibrating based on the singular values computed from the existing observations. To derive these algorithms fro","authors_text":"Cengiz Pehlevan, Dmitri B. Chklovskii, Yuansi Chen","cross_cats":["cs.NE","q-bio.NC","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-12-11T21:15:05Z","title":"Self-calibrating Neural Networks for Dimensionality Reduction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.03480","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a37b69d520b6e9ef66a7b40ae738d47f2db1f1a3ceae5215f77d44a6c22e9397","target":"record","created_at":"2026-05-18T00:48:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"9192aa2a40c4aa660bd1443d06661ff9b7a4f45a910cc7f8cffb628cb3aeab52","cross_cats_sorted":["cs.NE","q-bio.NC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-12-11T21:15:05Z","title_canon_sha256":"b93303ab3ed4a460674d186c6abfac29b074066eadda4fe8536d73808f045f2a"},"schema_version":"1.0","source":{"id":"1612.03480","kind":"arxiv","version":1}},"canonical_sha256":"1fed041905774f8854ab166a20adaf0c0124990943718b188bc41e78d3b29565","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1fed041905774f8854ab166a20adaf0c0124990943718b188bc41e78d3b29565","first_computed_at":"2026-05-18T00:48:26.033603Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:48:26.033603Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6PNCiH2RmZmqj3mYGKCfAqK8n61vWsQWMFFFi5mZ/Rlw9CrvhBNLdxhJ8kJfrb5/cx7KGRAb0hKW4z2ShRz2DA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:48:26.034109Z","signed_message":"canonical_sha256_bytes"},"source_id":"1612.03480","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a37b69d520b6e9ef66a7b40ae738d47f2db1f1a3ceae5215f77d44a6c22e9397","sha256:c71158ca81d3f8773ad69e78f9e24b24f5b3aefbdf63f56113f9c4007506fc72"],"state_sha256":"24bb648f2a49d0ff74811c41283b38f7dbb78d4a72d9be56228fb5e9a29fc929"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OflkxYI+21F7bhmrvrEK4CcyDAPlMaBna6uYrPnID8fdm++s2zJW8A7BqNYCKcnuIUSIgUBXegwSSzCO8QpWDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-22T12:22:22.551770Z","bundle_sha256":"b012e50dcfd045a72b705e171d2d1b27f7cb18887e5d7b2b1e6684e4105139cf"}}