{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:43Z3PP5FT67OCQCVSWMAOPQ3J7","short_pith_number":"pith:43Z3PP5F","canonical_record":{"source":{"id":"2302.07950","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-15T21:04:04Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"005794b5f6ddf3f8fd87200c7974c46579ac4b5919e6dca31df8bf6ca3c262e8","abstract_canon_sha256":"f8d3bd25b614128a94a0cb9b0829c94aceeb43c7cae480d7f516d3185b024202"},"schema_version":"1.0"},"canonical_sha256":"e6f3b7bfa59fbee140559598073e1b4fc2dc9cc1dde14ad382610d0a2369574e","source":{"kind":"arxiv","id":"2302.07950","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.07950","created_at":"2026-07-05T08:41:25Z"},{"alias_kind":"arxiv_version","alias_value":"2302.07950v2","created_at":"2026-07-05T08:41:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.07950","created_at":"2026-07-05T08:41:25Z"},{"alias_kind":"pith_short_12","alias_value":"43Z3PP5FT67O","created_at":"2026-07-05T08:41:25Z"},{"alias_kind":"pith_short_16","alias_value":"43Z3PP5FT67OCQCV","created_at":"2026-07-05T08:41:25Z"},{"alias_kind":"pith_short_8","alias_value":"43Z3PP5F","created_at":"2026-07-05T08:41:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:43Z3PP5FT67OCQCVSWMAOPQ3J7","target":"record","payload":{"canonical_record":{"source":{"id":"2302.07950","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-15T21:04:04Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"005794b5f6ddf3f8fd87200c7974c46579ac4b5919e6dca31df8bf6ca3c262e8","abstract_canon_sha256":"f8d3bd25b614128a94a0cb9b0829c94aceeb43c7cae480d7f516d3185b024202"},"schema_version":"1.0"},"canonical_sha256":"e6f3b7bfa59fbee140559598073e1b4fc2dc9cc1dde14ad382610d0a2369574e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:41:25.239899Z","signature_b64":"ggrCcsJcNXUeAfhoCwzCMFndk/KyB7Z/fcaVEj4liS5ZgFU9COXdXGGhq1eigiloNSCGK8ydOwVbl4cTuLH/Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e6f3b7bfa59fbee140559598073e1b4fc2dc9cc1dde14ad382610d0a2369574e","last_reissued_at":"2026-07-05T08:41:25.239487Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:41:25.239487Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2302.07950","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:41:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aMzqibx6HbUhi5t98SpUchoNAnK7od9vpKVBY+RcKq7q3tjiAAPFrqIpvv6HDb40GTCZOh9dQTwTeZEJ1iZcCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T09:30:36.508775Z"},"content_sha256":"869aaad6ba3be86ce7ff669c4c73296a2b3a6c8d3c407adb7de28916869539a8","schema_version":"1.0","event_id":"sha256:869aaad6ba3be86ce7ff669c4c73296a2b3a6c8d3c407adb7de28916869539a8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:43Z3PP5FT67OCQCVSWMAOPQ3J7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Self-Organising Neural Discrete Representation Learning \\`a la Kohonen","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"J\\\"urgen Schmidhuber, Kazuki Irie, R\\'obert Csord\\'as","submitted_at":"2023-02-15T21:04:04Z","abstract_excerpt":"Unsupervised learning of discrete representations in neural networks (NNs) from continuous ones is essential for many modern applications. Vector Quantisation (VQ) has become popular for this, in particular in the context of generative models, such as Variational Auto-Encoders (VAEs), where the exponential moving average-based VQ (EMA-VQ) algorithm is often used. Here, we study an alternative VQ algorithm based on Kohonen's learning rule for the Self-Organising Map (KSOM; 1982). EMA-VQ is a special case of KSOM. KSOM is known to offer two potential benefits: empirically, it converges faster th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.07950","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2302.07950/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T08:41:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dG3qW5cH1F/maM2nCry4RefgZ8FvnbItMzB7dca+VoYnmvA3jDbDiB6krR/96nfq+/YqysIOD4f///csG78aBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T09:30:36.509322Z"},"content_sha256":"10dcc719421ca264fac583b4c4d142438f84da5564497c1fe791b7dbe2f95506","schema_version":"1.0","event_id":"sha256:10dcc719421ca264fac583b4c4d142438f84da5564497c1fe791b7dbe2f95506"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/43Z3PP5FT67OCQCVSWMAOPQ3J7/bundle.json","state_url":"https://pith.science/pith/43Z3PP5FT67OCQCVSWMAOPQ3J7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/43Z3PP5FT67OCQCVSWMAOPQ3J7/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":"2026-08-21T09:30:36Z","links":{"resolver":"https://pith.science/pith/43Z3PP5FT67OCQCVSWMAOPQ3J7","bundle":"https://pith.science/pith/43Z3PP5FT67OCQCVSWMAOPQ3J7/bundle.json","state":"https://pith.science/pith/43Z3PP5FT67OCQCVSWMAOPQ3J7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/43Z3PP5FT67OCQCVSWMAOPQ3J7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:43Z3PP5FT67OCQCVSWMAOPQ3J7","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":"f8d3bd25b614128a94a0cb9b0829c94aceeb43c7cae480d7f516d3185b024202","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-15T21:04:04Z","title_canon_sha256":"005794b5f6ddf3f8fd87200c7974c46579ac4b5919e6dca31df8bf6ca3c262e8"},"schema_version":"1.0","source":{"id":"2302.07950","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.07950","created_at":"2026-07-05T08:41:25Z"},{"alias_kind":"arxiv_version","alias_value":"2302.07950v2","created_at":"2026-07-05T08:41:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.07950","created_at":"2026-07-05T08:41:25Z"},{"alias_kind":"pith_short_12","alias_value":"43Z3PP5FT67O","created_at":"2026-07-05T08:41:25Z"},{"alias_kind":"pith_short_16","alias_value":"43Z3PP5FT67OCQCV","created_at":"2026-07-05T08:41:25Z"},{"alias_kind":"pith_short_8","alias_value":"43Z3PP5F","created_at":"2026-07-05T08:41:25Z"}],"graph_snapshots":[{"event_id":"sha256:10dcc719421ca264fac583b4c4d142438f84da5564497c1fe791b7dbe2f95506","target":"graph","created_at":"2026-07-05T08:41:25Z","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/2302.07950/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Unsupervised learning of discrete representations in neural networks (NNs) from continuous ones is essential for many modern applications. Vector Quantisation (VQ) has become popular for this, in particular in the context of generative models, such as Variational Auto-Encoders (VAEs), where the exponential moving average-based VQ (EMA-VQ) algorithm is often used. Here, we study an alternative VQ algorithm based on Kohonen's learning rule for the Self-Organising Map (KSOM; 1982). EMA-VQ is a special case of KSOM. KSOM is known to offer two potential benefits: empirically, it converges faster th","authors_text":"J\\\"urgen Schmidhuber, Kazuki Irie, R\\'obert Csord\\'as","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-15T21:04:04Z","title":"Self-Organising Neural Discrete Representation Learning \\`a la Kohonen"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.07950","kind":"arxiv","version":2},"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":"sha256:869aaad6ba3be86ce7ff669c4c73296a2b3a6c8d3c407adb7de28916869539a8","target":"record","created_at":"2026-07-05T08:41:25Z","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":"f8d3bd25b614128a94a0cb9b0829c94aceeb43c7cae480d7f516d3185b024202","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-15T21:04:04Z","title_canon_sha256":"005794b5f6ddf3f8fd87200c7974c46579ac4b5919e6dca31df8bf6ca3c262e8"},"schema_version":"1.0","source":{"id":"2302.07950","kind":"arxiv","version":2}},"canonical_sha256":"e6f3b7bfa59fbee140559598073e1b4fc2dc9cc1dde14ad382610d0a2369574e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e6f3b7bfa59fbee140559598073e1b4fc2dc9cc1dde14ad382610d0a2369574e","first_computed_at":"2026-07-05T08:41:25.239487Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:41:25.239487Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ggrCcsJcNXUeAfhoCwzCMFndk/KyB7Z/fcaVEj4liS5ZgFU9COXdXGGhq1eigiloNSCGK8ydOwVbl4cTuLH/Bw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:41:25.239899Z","signed_message":"canonical_sha256_bytes"},"source_id":"2302.07950","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:869aaad6ba3be86ce7ff669c4c73296a2b3a6c8d3c407adb7de28916869539a8","sha256:10dcc719421ca264fac583b4c4d142438f84da5564497c1fe791b7dbe2f95506"],"state_sha256":"205735bf0b5fa41a67c9950459a4924e89cefde5a63518e2c14561e70fdb5d48"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9bDlkW1LQ+la9l57pL36VRr0G5cC/pxHFfU46vMFmWX9a07iwRbL07Jl+lWTCGzQ1ORQzWk9U/Ne1stkzjIWBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T09:30:36.513251Z","bundle_sha256":"d7cb2cdf42ee09501344ce4c1a00d88e604d2a825ac524888b94b6349eb55f05"}}