{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:DVOSTF3BCKI3RGKOGKOQMBHSRI","short_pith_number":"pith:DVOSTF3B","canonical_record":{"source":{"id":"1611.01967","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-11-07T10:15:40Z","cross_cats_sorted":["cs.NE"],"title_canon_sha256":"0dc040550fddb2d5bbd3ba2ec4b54c232d56ee4d515db599ea3b1fc9c573a230","abstract_canon_sha256":"0e73cef488ab5061f06fe3a0396f0cb0187f801a21954d5afcc803ec999ee34e"},"schema_version":"1.0"},"canonical_sha256":"1d5d2997611291b8994e329d0604f28a2924b5630de7a365619790916778af1a","source":{"kind":"arxiv","id":"1611.01967","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.01967","created_at":"2026-05-18T00:48:42Z"},{"alias_kind":"arxiv_version","alias_value":"1611.01967v2","created_at":"2026-05-18T00:48:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.01967","created_at":"2026-05-18T00:48:42Z"},{"alias_kind":"pith_short_12","alias_value":"DVOSTF3BCKI3","created_at":"2026-05-18T12:30:12Z"},{"alias_kind":"pith_short_16","alias_value":"DVOSTF3BCKI3RGKO","created_at":"2026-05-18T12:30:12Z"},{"alias_kind":"pith_short_8","alias_value":"DVOSTF3B","created_at":"2026-05-18T12:30:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:DVOSTF3BCKI3RGKOGKOQMBHSRI","target":"record","payload":{"canonical_record":{"source":{"id":"1611.01967","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-11-07T10:15:40Z","cross_cats_sorted":["cs.NE"],"title_canon_sha256":"0dc040550fddb2d5bbd3ba2ec4b54c232d56ee4d515db599ea3b1fc9c573a230","abstract_canon_sha256":"0e73cef488ab5061f06fe3a0396f0cb0187f801a21954d5afcc803ec999ee34e"},"schema_version":"1.0"},"canonical_sha256":"1d5d2997611291b8994e329d0604f28a2924b5630de7a365619790916778af1a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:48:42.173449Z","signature_b64":"zsTJRAQI42jTLSJrTgCtD/XBNaTUchxe5XgbYQBiF3QsqLx9xg21mTBuT8Nntr7lMvCIuzXxXfV81Xh6m79yBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1d5d2997611291b8994e329d0604f28a2924b5630de7a365619790916778af1a","last_reissued_at":"2026-05-18T00:48:42.172923Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:48:42.172923Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1611.01967","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":"2026-05-18T00:48:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vUHlhNfqDUo+beGsPcYPVmsV0WGDy0+mgWCBpturR+LJLBczQWZO4qpsJGzN1SjTG+TdgyoCGsBQbAyHD+e3AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T17:58:31.597556Z"},"content_sha256":"6f0c66b05072b844bc8d3b1c3e50486b82928e3a8e7efa9c182719880b996929","schema_version":"1.0","event_id":"sha256:6f0c66b05072b844bc8d3b1c3e50486b82928e3a8e7efa9c182719880b996929"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:DVOSTF3BCKI3RGKOGKOQMBHSRI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Regularizing CNNs with Locally Constrained Decorrelations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.LG","authors_text":"Guillem Cucurull, Jordi Gonz\\`alez, Josep M. Gonfaus, Pau Rodr\\'iguez, Xavier Roca","submitted_at":"2016-11-07T10:15:40Z","abstract_excerpt":"Regularization is key for deep learning since it allows training more complex models while keeping lower levels of overfitting. However, the most prevalent regularizations do not leverage all the capacity of the models since they rely on reducing the effective number of parameters. Feature decorrelation is an alternative for using the full capacity of the models but the overfitting reduction margins are too narrow given the overhead it introduces. In this paper, we show that regularizing negatively correlated features is an obstacle for effective decorrelation and present OrthoReg, a novel reg"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.01967","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":""},"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":"2026-05-18T00:48:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SXvHokyjqKZz9Uq/ulPqAtBq9YYVKBi8xuczVO8Vs2MkxhWrXV3WQTHFqgkml8W3s5vPh7Iw9QaLCYbQekObAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T17:58:31.597905Z"},"content_sha256":"a7cb32ca3e27c82d902d632dce413a6e97ff679fcc38015125566dfbc23c78a9","schema_version":"1.0","event_id":"sha256:a7cb32ca3e27c82d902d632dce413a6e97ff679fcc38015125566dfbc23c78a9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DVOSTF3BCKI3RGKOGKOQMBHSRI/bundle.json","state_url":"https://pith.science/pith/DVOSTF3BCKI3RGKOGKOQMBHSRI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DVOSTF3BCKI3RGKOGKOQMBHSRI/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":"2026-06-30T17:58:31Z","links":{"resolver":"https://pith.science/pith/DVOSTF3BCKI3RGKOGKOQMBHSRI","bundle":"https://pith.science/pith/DVOSTF3BCKI3RGKOGKOQMBHSRI/bundle.json","state":"https://pith.science/pith/DVOSTF3BCKI3RGKOGKOQMBHSRI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DVOSTF3BCKI3RGKOGKOQMBHSRI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:DVOSTF3BCKI3RGKOGKOQMBHSRI","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":"0e73cef488ab5061f06fe3a0396f0cb0187f801a21954d5afcc803ec999ee34e","cross_cats_sorted":["cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-11-07T10:15:40Z","title_canon_sha256":"0dc040550fddb2d5bbd3ba2ec4b54c232d56ee4d515db599ea3b1fc9c573a230"},"schema_version":"1.0","source":{"id":"1611.01967","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.01967","created_at":"2026-05-18T00:48:42Z"},{"alias_kind":"arxiv_version","alias_value":"1611.01967v2","created_at":"2026-05-18T00:48:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.01967","created_at":"2026-05-18T00:48:42Z"},{"alias_kind":"pith_short_12","alias_value":"DVOSTF3BCKI3","created_at":"2026-05-18T12:30:12Z"},{"alias_kind":"pith_short_16","alias_value":"DVOSTF3BCKI3RGKO","created_at":"2026-05-18T12:30:12Z"},{"alias_kind":"pith_short_8","alias_value":"DVOSTF3B","created_at":"2026-05-18T12:30:12Z"}],"graph_snapshots":[{"event_id":"sha256:a7cb32ca3e27c82d902d632dce413a6e97ff679fcc38015125566dfbc23c78a9","target":"graph","created_at":"2026-05-18T00:48:42Z","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":"Regularization is key for deep learning since it allows training more complex models while keeping lower levels of overfitting. However, the most prevalent regularizations do not leverage all the capacity of the models since they rely on reducing the effective number of parameters. Feature decorrelation is an alternative for using the full capacity of the models but the overfitting reduction margins are too narrow given the overhead it introduces. In this paper, we show that regularizing negatively correlated features is an obstacle for effective decorrelation and present OrthoReg, a novel reg","authors_text":"Guillem Cucurull, Jordi Gonz\\`alez, Josep M. Gonfaus, Pau Rodr\\'iguez, Xavier Roca","cross_cats":["cs.NE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-11-07T10:15:40Z","title":"Regularizing CNNs with Locally Constrained Decorrelations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.01967","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":"sha256:6f0c66b05072b844bc8d3b1c3e50486b82928e3a8e7efa9c182719880b996929","target":"record","created_at":"2026-05-18T00:48:42Z","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":"0e73cef488ab5061f06fe3a0396f0cb0187f801a21954d5afcc803ec999ee34e","cross_cats_sorted":["cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-11-07T10:15:40Z","title_canon_sha256":"0dc040550fddb2d5bbd3ba2ec4b54c232d56ee4d515db599ea3b1fc9c573a230"},"schema_version":"1.0","source":{"id":"1611.01967","kind":"arxiv","version":2}},"canonical_sha256":"1d5d2997611291b8994e329d0604f28a2924b5630de7a365619790916778af1a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1d5d2997611291b8994e329d0604f28a2924b5630de7a365619790916778af1a","first_computed_at":"2026-05-18T00:48:42.172923Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:48:42.172923Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zsTJRAQI42jTLSJrTgCtD/XBNaTUchxe5XgbYQBiF3QsqLx9xg21mTBuT8Nntr7lMvCIuzXxXfV81Xh6m79yBg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:48:42.173449Z","signed_message":"canonical_sha256_bytes"},"source_id":"1611.01967","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6f0c66b05072b844bc8d3b1c3e50486b82928e3a8e7efa9c182719880b996929","sha256:a7cb32ca3e27c82d902d632dce413a6e97ff679fcc38015125566dfbc23c78a9"],"state_sha256":"49cd9edf901aec7b0b6f1c732753e6227561156eeffe61afc016344c911a1573"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ldmwHtLZOMl7K/hluQL/DFTb+Hp35cacPPl5CZVLlZer5clNVHpEbR6bJy8q4r9IlMdh1Vh+PNm0XK/GkGnTBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-30T17:58:31.599863Z","bundle_sha256":"6c956d8dda86b32df4da3bc9c02b2c78bd646b7f374246e6ae498d3a3b2eb5e6"}}