{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:JLLSUNXHXHI4EZMRYZAQOT3CPQ","short_pith_number":"pith:JLLSUNXH","canonical_record":{"source":{"id":"2202.06749","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-02-10T23:32:26Z","cross_cats_sorted":[],"title_canon_sha256":"31d7c981f68577c7b4f54be93f07426dc8784f7cd119f29912be62312b1ee216","abstract_canon_sha256":"25280f5865a9cdc24cf52f9e44915b58e3b53748bb282e397932466faf4e25f5"},"schema_version":"1.0"},"canonical_sha256":"4ad72a36e7b9d1c26591c641074f627c16fe49bb173d4e05c089a87da5007b10","source":{"kind":"arxiv","id":"2202.06749","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.06749","created_at":"2026-07-05T03:58:36Z"},{"alias_kind":"arxiv_version","alias_value":"2202.06749v2","created_at":"2026-07-05T03:58:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.06749","created_at":"2026-07-05T03:58:36Z"},{"alias_kind":"pith_short_12","alias_value":"JLLSUNXHXHI4","created_at":"2026-07-05T03:58:36Z"},{"alias_kind":"pith_short_16","alias_value":"JLLSUNXHXHI4EZMR","created_at":"2026-07-05T03:58:36Z"},{"alias_kind":"pith_short_8","alias_value":"JLLSUNXH","created_at":"2026-07-05T03:58:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:JLLSUNXHXHI4EZMRYZAQOT3CPQ","target":"record","payload":{"canonical_record":{"source":{"id":"2202.06749","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-02-10T23:32:26Z","cross_cats_sorted":[],"title_canon_sha256":"31d7c981f68577c7b4f54be93f07426dc8784f7cd119f29912be62312b1ee216","abstract_canon_sha256":"25280f5865a9cdc24cf52f9e44915b58e3b53748bb282e397932466faf4e25f5"},"schema_version":"1.0"},"canonical_sha256":"4ad72a36e7b9d1c26591c641074f627c16fe49bb173d4e05c089a87da5007b10","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:58:36.463142Z","signature_b64":"piEcNGXkwXONOxTr5xX2Gc3lpLhkqlpE4QHP1ghHmx2HM59oL+y5D4IECyL6eBGB+eslFfRSsagniDU7eF9VDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4ad72a36e7b9d1c26591c641074f627c16fe49bb173d4e05c089a87da5007b10","last_reissued_at":"2026-07-05T03:58:36.462761Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:58:36.462761Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2202.06749","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":"2026-07-05T03:58:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fjPrCRtZoTj0arhkHyZjYAhPTEVX5EcHLF/tD2Dzra7aoDVyYoSL0eylT2RXvkKqAF/m9C8p2hIlKnWta5hwBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T03:01:57.770387Z"},"content_sha256":"49f6f00a4ed9d5ddb1259147e36197e3d921c3b3158c238e12a0711072630af8","schema_version":"1.0","event_id":"sha256:49f6f00a4ed9d5ddb1259147e36197e3d921c3b3158c238e12a0711072630af8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:JLLSUNXHXHI4EZMRYZAQOT3CPQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Information Flow in Deep Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ravid Shwartz-Ziv","submitted_at":"2022-02-10T23:32:26Z","abstract_excerpt":"Although deep neural networks have been immensely successful, there is no comprehensive theoretical understanding of how they work or are structured. As a result, deep networks are often seen as black boxes with unclear interpretations and reliability. Understanding the performance of deep neural networks is one of the greatest scientific challenges. This work aims to apply principles and techniques from information theory to deep learning models to increase our theoretical understanding and design better algorithms. We first describe our information-theoretic approach to deep learning. Then, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.06749","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/2202.06749/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":"2026-07-05T03:58:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bDPWWx4Wl6ZoqCrFuTIeQhLPHeuzF/0hiqREBWquzGV+oDdE2dwD+CGOk7bhASBt6XbbD/AxRpiV/Sfxpw8wDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T03:01:57.770767Z"},"content_sha256":"681707d0205948a2943b08d88faa0f14eca1e382cece224067a0f00978e2996f","schema_version":"1.0","event_id":"sha256:681707d0205948a2943b08d88faa0f14eca1e382cece224067a0f00978e2996f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JLLSUNXHXHI4EZMRYZAQOT3CPQ/bundle.json","state_url":"https://pith.science/pith/JLLSUNXHXHI4EZMRYZAQOT3CPQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JLLSUNXHXHI4EZMRYZAQOT3CPQ/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":"2026-08-17T03:01:57Z","links":{"resolver":"https://pith.science/pith/JLLSUNXHXHI4EZMRYZAQOT3CPQ","bundle":"https://pith.science/pith/JLLSUNXHXHI4EZMRYZAQOT3CPQ/bundle.json","state":"https://pith.science/pith/JLLSUNXHXHI4EZMRYZAQOT3CPQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JLLSUNXHXHI4EZMRYZAQOT3CPQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:JLLSUNXHXHI4EZMRYZAQOT3CPQ","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":"25280f5865a9cdc24cf52f9e44915b58e3b53748bb282e397932466faf4e25f5","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-02-10T23:32:26Z","title_canon_sha256":"31d7c981f68577c7b4f54be93f07426dc8784f7cd119f29912be62312b1ee216"},"schema_version":"1.0","source":{"id":"2202.06749","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.06749","created_at":"2026-07-05T03:58:36Z"},{"alias_kind":"arxiv_version","alias_value":"2202.06749v2","created_at":"2026-07-05T03:58:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.06749","created_at":"2026-07-05T03:58:36Z"},{"alias_kind":"pith_short_12","alias_value":"JLLSUNXHXHI4","created_at":"2026-07-05T03:58:36Z"},{"alias_kind":"pith_short_16","alias_value":"JLLSUNXHXHI4EZMR","created_at":"2026-07-05T03:58:36Z"},{"alias_kind":"pith_short_8","alias_value":"JLLSUNXH","created_at":"2026-07-05T03:58:36Z"}],"graph_snapshots":[{"event_id":"sha256:681707d0205948a2943b08d88faa0f14eca1e382cece224067a0f00978e2996f","target":"graph","created_at":"2026-07-05T03:58:36Z","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/2202.06749/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Although deep neural networks have been immensely successful, there is no comprehensive theoretical understanding of how they work or are structured. As a result, deep networks are often seen as black boxes with unclear interpretations and reliability. Understanding the performance of deep neural networks is one of the greatest scientific challenges. This work aims to apply principles and techniques from information theory to deep learning models to increase our theoretical understanding and design better algorithms. We first describe our information-theoretic approach to deep learning. Then, ","authors_text":"Ravid Shwartz-Ziv","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-02-10T23:32:26Z","title":"Information Flow in Deep Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.06749","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":"sha256:49f6f00a4ed9d5ddb1259147e36197e3d921c3b3158c238e12a0711072630af8","target":"record","created_at":"2026-07-05T03:58:36Z","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":"25280f5865a9cdc24cf52f9e44915b58e3b53748bb282e397932466faf4e25f5","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-02-10T23:32:26Z","title_canon_sha256":"31d7c981f68577c7b4f54be93f07426dc8784f7cd119f29912be62312b1ee216"},"schema_version":"1.0","source":{"id":"2202.06749","kind":"arxiv","version":2}},"canonical_sha256":"4ad72a36e7b9d1c26591c641074f627c16fe49bb173d4e05c089a87da5007b10","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4ad72a36e7b9d1c26591c641074f627c16fe49bb173d4e05c089a87da5007b10","first_computed_at":"2026-07-05T03:58:36.462761Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:58:36.462761Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"piEcNGXkwXONOxTr5xX2Gc3lpLhkqlpE4QHP1ghHmx2HM59oL+y5D4IECyL6eBGB+eslFfRSsagniDU7eF9VDA==","signature_status":"signed_v1","signed_at":"2026-07-05T03:58:36.463142Z","signed_message":"canonical_sha256_bytes"},"source_id":"2202.06749","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:49f6f00a4ed9d5ddb1259147e36197e3d921c3b3158c238e12a0711072630af8","sha256:681707d0205948a2943b08d88faa0f14eca1e382cece224067a0f00978e2996f"],"state_sha256":"755b11c21d8bdbd5025169c89526528455e9a7e54637407f15b8379c2a84515a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"E2k9N0lAlHK+uioMiE9JAVSU85/2DiCLNdBtP9pAPQVnciAv4aGoYVHroXljIghhszRlkZJt9vkj9wtNukXXBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T03:01:57.773289Z","bundle_sha256":"1926e33ced16f0f17d2d090d3c51f0148c940d3a94622cefae066c4866ad3290"}}