{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:NQGJAP4XFPNHN3DTOCHL3YNDCL","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":"28cbdbeb929718fb83422603aa1a482544c992811923883cabd072ec2bcf0f28","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-08-21T23:43:30Z","title_canon_sha256":"c72529b7a5f3bfb331bb5e1ba0e18d18407e253a683829b69bf02507b56ada8e"},"schema_version":"1.0","source":{"id":"1908.08145","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.08145","created_at":"2026-07-05T00:13:16Z"},{"alias_kind":"arxiv_version","alias_value":"1908.08145v1","created_at":"2026-07-05T00:13:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.08145","created_at":"2026-07-05T00:13:16Z"},{"alias_kind":"pith_short_12","alias_value":"NQGJAP4XFPNH","created_at":"2026-07-05T00:13:16Z"},{"alias_kind":"pith_short_16","alias_value":"NQGJAP4XFPNHN3DT","created_at":"2026-07-05T00:13:16Z"},{"alias_kind":"pith_short_8","alias_value":"NQGJAP4X","created_at":"2026-07-05T00:13:16Z"}],"graph_snapshots":[{"event_id":"sha256:cfc8d92986cdd0be78dbb825b8db626969a6eaab320eb3c87478b35dadecb6ef","target":"graph","created_at":"2026-07-05T00:13:16Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1908.08145/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Semi-supervised learning algorithms typically construct a weighted graph of data points to represent a manifold. However, an explicit graph representation is problematic for neural networks operating in the online setting. Here, we propose a feed-forward neural network capable of semi-supervised learning on manifolds without using an explicit graph representation. Our algorithm uses channels that represent localities on the manifold such that correlations between channels represent manifold structure. The proposed neural network has two layers. The first layer learns to build a representation ","authors_text":"Alexander Genkin, Anirvan M. Sengupta, Dmitri Chklovskii","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-08-21T23:43:30Z","title":"A Neural Network for Semi-Supervised Learning on Manifolds"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.08145","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":"sha256:737d98b0855dc53c6133a9bc3060a1cbbe0b8e9a4abe626ba1c8bb2701ad48f6","target":"record","created_at":"2026-07-05T00:13:16Z","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":"28cbdbeb929718fb83422603aa1a482544c992811923883cabd072ec2bcf0f28","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-08-21T23:43:30Z","title_canon_sha256":"c72529b7a5f3bfb331bb5e1ba0e18d18407e253a683829b69bf02507b56ada8e"},"schema_version":"1.0","source":{"id":"1908.08145","kind":"arxiv","version":1}},"canonical_sha256":"6c0c903f972bda76ec73708ebde1a312dc58e7335073f97d3ffc795e8b1f48e4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6c0c903f972bda76ec73708ebde1a312dc58e7335073f97d3ffc795e8b1f48e4","first_computed_at":"2026-07-05T00:13:16.200982Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:13:16.200982Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cHVBGv7nDDRc3pvQjxA9A+w2VesXfIFzltotHTPn78Y05KZTrrr71bpk+IeLVKLR8lqFO/iMhhUV2TgaTpXACw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:13:16.201337Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.08145","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:737d98b0855dc53c6133a9bc3060a1cbbe0b8e9a4abe626ba1c8bb2701ad48f6","sha256:cfc8d92986cdd0be78dbb825b8db626969a6eaab320eb3c87478b35dadecb6ef"],"state_sha256":"ab0a710ef76371f45024ad925432e381dff1395c3a925af88f85b6ede36e9073"}