{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:AOCRJIRB3DBTRNWK6WDRTMXUAU","short_pith_number":"pith:AOCRJIRB","canonical_record":{"source":{"id":"2003.12537","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-27T17:05:03Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"5539c02ba99c8d5df60340a8f948423bbd0a19204ac59b576ab07ae576f144df","abstract_canon_sha256":"c2ad3479f6c831643da7825defd6eccc9327ab4937a7e03c8fda8ad54fa63804"},"schema_version":"1.0"},"canonical_sha256":"038514a221d8c338b6caf58719b2f40520924085d320b181b22d589f7c3313af","source":{"kind":"arxiv","id":"2003.12537","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2003.12537","created_at":"2026-07-05T02:04:40Z"},{"alias_kind":"arxiv_version","alias_value":"2003.12537v3","created_at":"2026-07-05T02:04:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.12537","created_at":"2026-07-05T02:04:40Z"},{"alias_kind":"pith_short_12","alias_value":"AOCRJIRB3DBT","created_at":"2026-07-05T02:04:40Z"},{"alias_kind":"pith_short_16","alias_value":"AOCRJIRB3DBTRNWK","created_at":"2026-07-05T02:04:40Z"},{"alias_kind":"pith_short_8","alias_value":"AOCRJIRB","created_at":"2026-07-05T02:04:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:AOCRJIRB3DBTRNWK6WDRTMXUAU","target":"record","payload":{"canonical_record":{"source":{"id":"2003.12537","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-27T17:05:03Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"5539c02ba99c8d5df60340a8f948423bbd0a19204ac59b576ab07ae576f144df","abstract_canon_sha256":"c2ad3479f6c831643da7825defd6eccc9327ab4937a7e03c8fda8ad54fa63804"},"schema_version":"1.0"},"canonical_sha256":"038514a221d8c338b6caf58719b2f40520924085d320b181b22d589f7c3313af","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:04:40.052969Z","signature_b64":"PGSpTUjsLqhLy4h5WqG9jPBiQLOGEDheZn1UZQx8LRinLNbunJly5z0jHVHI9gMWAoSiTDN5wQDsiLuyzzMOBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"038514a221d8c338b6caf58719b2f40520924085d320b181b22d589f7c3313af","last_reissued_at":"2026-07-05T02:04:40.052440Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:04:40.052440Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2003.12537","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:04:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+sVspeJWkiDR0NTvxtBi3WG4kXION7DBR583yxaT1qB9t7XPldgieQqnqOqdqc8j1HIyC+x1umXbvJY44GbJDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T20:33:03.584268Z"},"content_sha256":"cb29c39003d1a01511ee16d8ce6a824b65ed264ab83c72f4b9628f21098c8b05","schema_version":"1.0","event_id":"sha256:cb29c39003d1a01511ee16d8ce6a824b65ed264ab83c72f4b9628f21098c8b05"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:AOCRJIRB3DBTRNWK6WDRTMXUAU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Andreas Kirsch, Clare Lyle, Yarin Gal","submitted_at":"2020-03-27T17:05:03Z","abstract_excerpt":"The Information Bottleneck principle offers both a mechanism to explain how deep neural networks train and generalize, as well as a regularized objective with which to train models. However, multiple competing objectives are proposed in the literature, and the information-theoretic quantities used in these objectives are difficult to compute for large deep neural networks, which in turn limits their use as a training objective. In this work, we review these quantities and compare and unify previously proposed objectives, which allows us to develop surrogate objectives more friendly to optimiza"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.12537","kind":"arxiv","version":3},"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/2003.12537/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":"2026-07-05T02:04:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lMZCpW9F/z8KhLNseUJXWJHHwi/kZI+k+qMGNnJEVZisqfP9QGcPecpCzOnEiyiuvTCanW/OGciFrxY28Oy4CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T20:33:03.584680Z"},"content_sha256":"76f8346a3509a3cc19ed98a2e93cccd16d707d1a8859db9a2b56e1b622bf3252","schema_version":"1.0","event_id":"sha256:76f8346a3509a3cc19ed98a2e93cccd16d707d1a8859db9a2b56e1b622bf3252"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AOCRJIRB3DBTRNWK6WDRTMXUAU/bundle.json","state_url":"https://pith.science/pith/AOCRJIRB3DBTRNWK6WDRTMXUAU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AOCRJIRB3DBTRNWK6WDRTMXUAU/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":"2026-08-03T20:33:03Z","links":{"resolver":"https://pith.science/pith/AOCRJIRB3DBTRNWK6WDRTMXUAU","bundle":"https://pith.science/pith/AOCRJIRB3DBTRNWK6WDRTMXUAU/bundle.json","state":"https://pith.science/pith/AOCRJIRB3DBTRNWK6WDRTMXUAU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AOCRJIRB3DBTRNWK6WDRTMXUAU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:AOCRJIRB3DBTRNWK6WDRTMXUAU","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":"c2ad3479f6c831643da7825defd6eccc9327ab4937a7e03c8fda8ad54fa63804","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-27T17:05:03Z","title_canon_sha256":"5539c02ba99c8d5df60340a8f948423bbd0a19204ac59b576ab07ae576f144df"},"schema_version":"1.0","source":{"id":"2003.12537","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2003.12537","created_at":"2026-07-05T02:04:40Z"},{"alias_kind":"arxiv_version","alias_value":"2003.12537v3","created_at":"2026-07-05T02:04:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.12537","created_at":"2026-07-05T02:04:40Z"},{"alias_kind":"pith_short_12","alias_value":"AOCRJIRB3DBT","created_at":"2026-07-05T02:04:40Z"},{"alias_kind":"pith_short_16","alias_value":"AOCRJIRB3DBTRNWK","created_at":"2026-07-05T02:04:40Z"},{"alias_kind":"pith_short_8","alias_value":"AOCRJIRB","created_at":"2026-07-05T02:04:40Z"}],"graph_snapshots":[{"event_id":"sha256:76f8346a3509a3cc19ed98a2e93cccd16d707d1a8859db9a2b56e1b622bf3252","target":"graph","created_at":"2026-07-05T02:04:40Z","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/2003.12537/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The Information Bottleneck principle offers both a mechanism to explain how deep neural networks train and generalize, as well as a regularized objective with which to train models. However, multiple competing objectives are proposed in the literature, and the information-theoretic quantities used in these objectives are difficult to compute for large deep neural networks, which in turn limits their use as a training objective. In this work, we review these quantities and compare and unify previously proposed objectives, which allows us to develop surrogate objectives more friendly to optimiza","authors_text":"Andreas Kirsch, Clare Lyle, Yarin Gal","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-27T17:05:03Z","title":"Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.12537","kind":"arxiv","version":3},"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:cb29c39003d1a01511ee16d8ce6a824b65ed264ab83c72f4b9628f21098c8b05","target":"record","created_at":"2026-07-05T02:04:40Z","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":"c2ad3479f6c831643da7825defd6eccc9327ab4937a7e03c8fda8ad54fa63804","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-27T17:05:03Z","title_canon_sha256":"5539c02ba99c8d5df60340a8f948423bbd0a19204ac59b576ab07ae576f144df"},"schema_version":"1.0","source":{"id":"2003.12537","kind":"arxiv","version":3}},"canonical_sha256":"038514a221d8c338b6caf58719b2f40520924085d320b181b22d589f7c3313af","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"038514a221d8c338b6caf58719b2f40520924085d320b181b22d589f7c3313af","first_computed_at":"2026-07-05T02:04:40.052440Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:04:40.052440Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PGSpTUjsLqhLy4h5WqG9jPBiQLOGEDheZn1UZQx8LRinLNbunJly5z0jHVHI9gMWAoSiTDN5wQDsiLuyzzMOBg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:04:40.052969Z","signed_message":"canonical_sha256_bytes"},"source_id":"2003.12537","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cb29c39003d1a01511ee16d8ce6a824b65ed264ab83c72f4b9628f21098c8b05","sha256:76f8346a3509a3cc19ed98a2e93cccd16d707d1a8859db9a2b56e1b622bf3252"],"state_sha256":"40303f4a63e803ec7740f696a7adc921f87a8db7b4417626c6c7d44b10e6665c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ehgj/xnZ8FlN71FPUX9D7FwJ2UKEYCVSIQ+aP+QSr29gK/thUBL6emiJy5jqmlP7bAb09xk3MSiY5D8Svqn+BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T20:33:03.588614Z","bundle_sha256":"b7ee4e4d6c5d535d50d61ca1f3d15c8a712403f5d75ef7f697f4ebfbb42c1e1f"}}