{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2013:IPSZLZHDWKHHJFTXMVTG2RUE3M","short_pith_number":"pith:IPSZLZHD","canonical_record":{"source":{"id":"1301.3584","kind":"arxiv","version":7},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2013-01-16T04:47:02Z","cross_cats_sorted":["cs.NA"],"title_canon_sha256":"25709ee60081e4b4893f026bc1c89c75c0a0a9f38314de09ee2ab45599d4a427","abstract_canon_sha256":"f0eccd6f4c3c86edaad6ac332adf3b7b71980a35995824cea6fac71f8bc934c8"},"schema_version":"1.0"},"canonical_sha256":"43e595e4e3b28e74967765666d4684db2054514f7fe53f3fb8a55858907025f6","source":{"kind":"arxiv","id":"1301.3584","version":7},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1301.3584","created_at":"2026-05-18T02:58:57Z"},{"alias_kind":"arxiv_version","alias_value":"1301.3584v7","created_at":"2026-05-18T02:58:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1301.3584","created_at":"2026-05-18T02:58:57Z"},{"alias_kind":"pith_short_12","alias_value":"IPSZLZHDWKHH","created_at":"2026-05-18T12:27:46Z"},{"alias_kind":"pith_short_16","alias_value":"IPSZLZHDWKHHJFTX","created_at":"2026-05-18T12:27:46Z"},{"alias_kind":"pith_short_8","alias_value":"IPSZLZHD","created_at":"2026-05-18T12:27:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2013:IPSZLZHDWKHHJFTXMVTG2RUE3M","target":"record","payload":{"canonical_record":{"source":{"id":"1301.3584","kind":"arxiv","version":7},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2013-01-16T04:47:02Z","cross_cats_sorted":["cs.NA"],"title_canon_sha256":"25709ee60081e4b4893f026bc1c89c75c0a0a9f38314de09ee2ab45599d4a427","abstract_canon_sha256":"f0eccd6f4c3c86edaad6ac332adf3b7b71980a35995824cea6fac71f8bc934c8"},"schema_version":"1.0"},"canonical_sha256":"43e595e4e3b28e74967765666d4684db2054514f7fe53f3fb8a55858907025f6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:58:57.462134Z","signature_b64":"HGr8a4XRSSinxfnOcRuV1rpvKH9+8whwOfNDU7s2jyjbhq68TvGFapeB9L+gzgETWAzcOhxxbcRDHAPD2Y5XDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"43e595e4e3b28e74967765666d4684db2054514f7fe53f3fb8a55858907025f6","last_reissued_at":"2026-05-18T02:58:57.461367Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:58:57.461367Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1301.3584","source_version":7,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:58:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lS8CXBI2iwzQy2aeYOi0q5MUwR3SNzqu1LgRdhpWy5QtqlJ/42gZKZCGPFcUtlUjmoUkNv1fbVrDPVUeqGCmBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-13T16:09:19.966418Z"},"content_sha256":"85d017c4d2b6a6786d8e989a19a623c74a29beedee64e9165b218da5b8f9ef4e","schema_version":"1.0","event_id":"sha256:85d017c4d2b6a6786d8e989a19a623c74a29beedee64e9165b218da5b8f9ef4e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2013:IPSZLZHDWKHHJFTXMVTG2RUE3M","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Revisiting Natural Gradient for Deep Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"cs.LG","authors_text":"Razvan Pascanu, Yoshua Bengio","submitted_at":"2013-01-16T04:47:02Z","abstract_excerpt":"We evaluate natural gradient, an algorithm originally proposed in Amari (1997), for learning deep models. The contributions of this paper are as follows. We show the connection between natural gradient and three other recently proposed methods for training deep models: Hessian-Free (Martens, 2010), Krylov Subspace Descent (Vinyals and Povey, 2012) and TONGA (Le Roux et al., 2008). We describe how one can use unlabeled data to improve the generalization error obtained by natural gradient and empirically evaluate the robustness of the algorithm to the ordering of the training set compared to sto"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1301.3584","kind":"arxiv","version":7},"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":"2026-05-18T02:58:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qE8A0oHyeQCT1JW+Xdu+x9dsuUrqZi0oLQhlqv0i0J19OM3IdLDthxqt+gYIuWRXxlxEyoUkW5RrWNx6QFvzBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-13T16:09:19.967033Z"},"content_sha256":"2a014877602213ac0f059ca65e0e765aa2f8afbd65f09b0f11327e85a39c883e","schema_version":"1.0","event_id":"sha256:2a014877602213ac0f059ca65e0e765aa2f8afbd65f09b0f11327e85a39c883e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IPSZLZHDWKHHJFTXMVTG2RUE3M/bundle.json","state_url":"https://pith.science/pith/IPSZLZHDWKHHJFTXMVTG2RUE3M/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IPSZLZHDWKHHJFTXMVTG2RUE3M/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":"2026-07-13T16:09:19Z","links":{"resolver":"https://pith.science/pith/IPSZLZHDWKHHJFTXMVTG2RUE3M","bundle":"https://pith.science/pith/IPSZLZHDWKHHJFTXMVTG2RUE3M/bundle.json","state":"https://pith.science/pith/IPSZLZHDWKHHJFTXMVTG2RUE3M/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IPSZLZHDWKHHJFTXMVTG2RUE3M/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:IPSZLZHDWKHHJFTXMVTG2RUE3M","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":"f0eccd6f4c3c86edaad6ac332adf3b7b71980a35995824cea6fac71f8bc934c8","cross_cats_sorted":["cs.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2013-01-16T04:47:02Z","title_canon_sha256":"25709ee60081e4b4893f026bc1c89c75c0a0a9f38314de09ee2ab45599d4a427"},"schema_version":"1.0","source":{"id":"1301.3584","kind":"arxiv","version":7}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1301.3584","created_at":"2026-05-18T02:58:57Z"},{"alias_kind":"arxiv_version","alias_value":"1301.3584v7","created_at":"2026-05-18T02:58:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1301.3584","created_at":"2026-05-18T02:58:57Z"},{"alias_kind":"pith_short_12","alias_value":"IPSZLZHDWKHH","created_at":"2026-05-18T12:27:46Z"},{"alias_kind":"pith_short_16","alias_value":"IPSZLZHDWKHHJFTX","created_at":"2026-05-18T12:27:46Z"},{"alias_kind":"pith_short_8","alias_value":"IPSZLZHD","created_at":"2026-05-18T12:27:46Z"}],"graph_snapshots":[{"event_id":"sha256:2a014877602213ac0f059ca65e0e765aa2f8afbd65f09b0f11327e85a39c883e","target":"graph","created_at":"2026-05-18T02:58:57Z","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":"We evaluate natural gradient, an algorithm originally proposed in Amari (1997), for learning deep models. The contributions of this paper are as follows. We show the connection between natural gradient and three other recently proposed methods for training deep models: Hessian-Free (Martens, 2010), Krylov Subspace Descent (Vinyals and Povey, 2012) and TONGA (Le Roux et al., 2008). We describe how one can use unlabeled data to improve the generalization error obtained by natural gradient and empirically evaluate the robustness of the algorithm to the ordering of the training set compared to sto","authors_text":"Razvan Pascanu, Yoshua Bengio","cross_cats":["cs.NA"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2013-01-16T04:47:02Z","title":"Revisiting Natural Gradient for Deep Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1301.3584","kind":"arxiv","version":7},"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":"sha256:85d017c4d2b6a6786d8e989a19a623c74a29beedee64e9165b218da5b8f9ef4e","target":"record","created_at":"2026-05-18T02:58:57Z","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":"f0eccd6f4c3c86edaad6ac332adf3b7b71980a35995824cea6fac71f8bc934c8","cross_cats_sorted":["cs.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2013-01-16T04:47:02Z","title_canon_sha256":"25709ee60081e4b4893f026bc1c89c75c0a0a9f38314de09ee2ab45599d4a427"},"schema_version":"1.0","source":{"id":"1301.3584","kind":"arxiv","version":7}},"canonical_sha256":"43e595e4e3b28e74967765666d4684db2054514f7fe53f3fb8a55858907025f6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"43e595e4e3b28e74967765666d4684db2054514f7fe53f3fb8a55858907025f6","first_computed_at":"2026-05-18T02:58:57.461367Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:58:57.461367Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HGr8a4XRSSinxfnOcRuV1rpvKH9+8whwOfNDU7s2jyjbhq68TvGFapeB9L+gzgETWAzcOhxxbcRDHAPD2Y5XDg==","signature_status":"signed_v1","signed_at":"2026-05-18T02:58:57.462134Z","signed_message":"canonical_sha256_bytes"},"source_id":"1301.3584","source_kind":"arxiv","source_version":7}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:85d017c4d2b6a6786d8e989a19a623c74a29beedee64e9165b218da5b8f9ef4e","sha256:2a014877602213ac0f059ca65e0e765aa2f8afbd65f09b0f11327e85a39c883e"],"state_sha256":"8dc652a189b077ac339470d2f7b75a36721423ff574aff9ea7d1ed30b1d303db"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oPeHc51TgSie4taHu/zR6TelgmIA3pilua/MLFlHJ2Pg+A2hbYdnT/1F+abyqDxNNEVeRMJAB9tXSwplj/89Cg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-13T16:09:19.969480Z","bundle_sha256":"f254ec40c8621618e95297406e1ea001a837cf790e63c61204b7e8d39e2836e2"}}