{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:VPOHGOQXHTODKVJTCTU2DCHCKI","short_pith_number":"pith:VPOHGOQX","canonical_record":{"source":{"id":"2201.13415","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2022-01-31T18:20:43Z","cross_cats_sorted":[],"title_canon_sha256":"368e8aebce801e91800835f588dfc8fd5be0046130b7e03db65524eca473625c","abstract_canon_sha256":"9be6e7a32f447df8803c474b30b08c8de16aa4e9c9704def21c52ca4c0e42fc3"},"schema_version":"1.0"},"canonical_sha256":"abdc733a173cdc35553314e9a188e2520ace08f03ef4537551a776a7d930d925","source":{"kind":"arxiv","id":"2201.13415","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2201.13415","created_at":"2026-07-05T03:52:51Z"},{"alias_kind":"arxiv_version","alias_value":"2201.13415v1","created_at":"2026-07-05T03:52:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.13415","created_at":"2026-07-05T03:52:51Z"},{"alias_kind":"pith_short_12","alias_value":"VPOHGOQXHTOD","created_at":"2026-07-05T03:52:51Z"},{"alias_kind":"pith_short_16","alias_value":"VPOHGOQXHTODKVJT","created_at":"2026-07-05T03:52:51Z"},{"alias_kind":"pith_short_8","alias_value":"VPOHGOQX","created_at":"2026-07-05T03:52:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:VPOHGOQXHTODKVJTCTU2DCHCKI","target":"record","payload":{"canonical_record":{"source":{"id":"2201.13415","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2022-01-31T18:20:43Z","cross_cats_sorted":[],"title_canon_sha256":"368e8aebce801e91800835f588dfc8fd5be0046130b7e03db65524eca473625c","abstract_canon_sha256":"9be6e7a32f447df8803c474b30b08c8de16aa4e9c9704def21c52ca4c0e42fc3"},"schema_version":"1.0"},"canonical_sha256":"abdc733a173cdc35553314e9a188e2520ace08f03ef4537551a776a7d930d925","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:52:51.795779Z","signature_b64":"rgVPUXoLzBND81gtqXGLwEGFJJKXho2TyGNLWtn65bWUp38TbGMYFQl3hWIV/NvwF4N0MzG/7ZnnznLKS0IqBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"abdc733a173cdc35553314e9a188e2520ace08f03ef4537551a776a7d930d925","last_reissued_at":"2026-07-05T03:52:51.795261Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:52:51.795261Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2201.13415","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":"2026-07-05T03:52:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rFEC1GiNCx4nzmE4hX7eSs2eAD9/s/xV04mAWyDZ7mnKgriwNahHxroqj+ymBBPy47qvit7j60ARJjR6ReRBAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-31T13:27:33.769279Z"},"content_sha256":"b42f986d6b6960d2b665e5e9518ee140fd54c5770be4387090c6688ef632dc0b","schema_version":"1.0","event_id":"sha256:b42f986d6b6960d2b665e5e9518ee140fd54c5770be4387090c6688ef632dc0b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:VPOHGOQXHTODKVJTCTU2DCHCKI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Towards Scaling Difference Target Propagation by Learning Backprop Targets","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Abhinav Moudgil, Blake Richards, Eugene Belilovsky, Fabrice Normandin, Irina Rish, Maxence Ernoult, Sean Spinney, Yoshua Bengio","submitted_at":"2022-01-31T18:20:43Z","abstract_excerpt":"The development of biologically-plausible learning algorithms is important for understanding learning in the brain, but most of them fail to scale-up to real-world tasks, limiting their potential as explanations for learning by real brains. As such, it is important to explore learning algorithms that come with strong theoretical guarantees and can match the performance of backpropagation (BP) on complex tasks. One such algorithm is Difference Target Propagation (DTP), a biologically-plausible learning algorithm whose close relation with Gauss-Newton (GN) optimization has been recently establis"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.13415","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2201.13415/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":"2026-07-05T03:52:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aKdkb2goKfDyvrzF2NPe2XghDUp1ODJsxfTSKkAwGQByWm31fKjTsHOu61DraW2BMYzndX58XX6tjx8gv+2rAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-31T13:27:33.769680Z"},"content_sha256":"72b011204b72140d84974d3bbfaee3bc8b71fc5fd558939fb90ac8ac6fd1bbff","schema_version":"1.0","event_id":"sha256:72b011204b72140d84974d3bbfaee3bc8b71fc5fd558939fb90ac8ac6fd1bbff"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VPOHGOQXHTODKVJTCTU2DCHCKI/bundle.json","state_url":"https://pith.science/pith/VPOHGOQXHTODKVJTCTU2DCHCKI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VPOHGOQXHTODKVJTCTU2DCHCKI/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":"2026-07-31T13:27:33Z","links":{"resolver":"https://pith.science/pith/VPOHGOQXHTODKVJTCTU2DCHCKI","bundle":"https://pith.science/pith/VPOHGOQXHTODKVJTCTU2DCHCKI/bundle.json","state":"https://pith.science/pith/VPOHGOQXHTODKVJTCTU2DCHCKI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VPOHGOQXHTODKVJTCTU2DCHCKI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:VPOHGOQXHTODKVJTCTU2DCHCKI","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":"9be6e7a32f447df8803c474b30b08c8de16aa4e9c9704def21c52ca4c0e42fc3","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2022-01-31T18:20:43Z","title_canon_sha256":"368e8aebce801e91800835f588dfc8fd5be0046130b7e03db65524eca473625c"},"schema_version":"1.0","source":{"id":"2201.13415","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2201.13415","created_at":"2026-07-05T03:52:51Z"},{"alias_kind":"arxiv_version","alias_value":"2201.13415v1","created_at":"2026-07-05T03:52:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.13415","created_at":"2026-07-05T03:52:51Z"},{"alias_kind":"pith_short_12","alias_value":"VPOHGOQXHTOD","created_at":"2026-07-05T03:52:51Z"},{"alias_kind":"pith_short_16","alias_value":"VPOHGOQXHTODKVJT","created_at":"2026-07-05T03:52:51Z"},{"alias_kind":"pith_short_8","alias_value":"VPOHGOQX","created_at":"2026-07-05T03:52:51Z"}],"graph_snapshots":[{"event_id":"sha256:72b011204b72140d84974d3bbfaee3bc8b71fc5fd558939fb90ac8ac6fd1bbff","target":"graph","created_at":"2026-07-05T03:52:51Z","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/2201.13415/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The development of biologically-plausible learning algorithms is important for understanding learning in the brain, but most of them fail to scale-up to real-world tasks, limiting their potential as explanations for learning by real brains. As such, it is important to explore learning algorithms that come with strong theoretical guarantees and can match the performance of backpropagation (BP) on complex tasks. One such algorithm is Difference Target Propagation (DTP), a biologically-plausible learning algorithm whose close relation with Gauss-Newton (GN) optimization has been recently establis","authors_text":"Abhinav Moudgil, Blake Richards, Eugene Belilovsky, Fabrice Normandin, Irina Rish, Maxence Ernoult, Sean Spinney, Yoshua Bengio","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2022-01-31T18:20:43Z","title":"Towards Scaling Difference Target Propagation by Learning Backprop Targets"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.13415","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:b42f986d6b6960d2b665e5e9518ee140fd54c5770be4387090c6688ef632dc0b","target":"record","created_at":"2026-07-05T03:52:51Z","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":"9be6e7a32f447df8803c474b30b08c8de16aa4e9c9704def21c52ca4c0e42fc3","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2022-01-31T18:20:43Z","title_canon_sha256":"368e8aebce801e91800835f588dfc8fd5be0046130b7e03db65524eca473625c"},"schema_version":"1.0","source":{"id":"2201.13415","kind":"arxiv","version":1}},"canonical_sha256":"abdc733a173cdc35553314e9a188e2520ace08f03ef4537551a776a7d930d925","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"abdc733a173cdc35553314e9a188e2520ace08f03ef4537551a776a7d930d925","first_computed_at":"2026-07-05T03:52:51.795261Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:52:51.795261Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rgVPUXoLzBND81gtqXGLwEGFJJKXho2TyGNLWtn65bWUp38TbGMYFQl3hWIV/NvwF4N0MzG/7ZnnznLKS0IqBg==","signature_status":"signed_v1","signed_at":"2026-07-05T03:52:51.795779Z","signed_message":"canonical_sha256_bytes"},"source_id":"2201.13415","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b42f986d6b6960d2b665e5e9518ee140fd54c5770be4387090c6688ef632dc0b","sha256:72b011204b72140d84974d3bbfaee3bc8b71fc5fd558939fb90ac8ac6fd1bbff"],"state_sha256":"7b000c79e366d317ba0ee917f9149060ec90c441ee2677e625e36e4059faf04e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mFv5BWm/U4CzwpePkx48qkJFoVFVYohodpHIrW8138ZZUQ46+1ZVbICRpjhwVawOjLVwQ8YrgdjmT2I00zuaAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-31T13:27:33.773548Z","bundle_sha256":"dc38a09c28ec3e9707dbfafb238b6486f9f52f648059c67bd301ff61ab4b4883"}}