{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:L7RPBAHDLGFOANC7AWFK7MQ7FX","short_pith_number":"pith:L7RPBAHD","canonical_record":{"source":{"id":"1712.00003","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-29T23:09:58Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"771446ad8bbd2f8cd59cd591130f79bd8425be5f1fd2f8a239c7e39e973b6b7f","abstract_canon_sha256":"df9b35f7ae73b632bfceb569a2658126e5dc1ddb1442c7ad8a65189455f86567"},"schema_version":"1.0"},"canonical_sha256":"5fe2f080e3598ae0345f058aafb21f2dee8dfc19d76895f384d9fb20064381c0","source":{"kind":"arxiv","id":"1712.00003","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.00003","created_at":"2026-05-18T00:29:07Z"},{"alias_kind":"arxiv_version","alias_value":"1712.00003v1","created_at":"2026-05-18T00:29:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.00003","created_at":"2026-05-18T00:29:07Z"},{"alias_kind":"pith_short_12","alias_value":"L7RPBAHDLGFO","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_16","alias_value":"L7RPBAHDLGFOANC7","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_8","alias_value":"L7RPBAHD","created_at":"2026-05-18T12:31:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:L7RPBAHDLGFOANC7AWFK7MQ7FX","target":"record","payload":{"canonical_record":{"source":{"id":"1712.00003","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-29T23:09:58Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"771446ad8bbd2f8cd59cd591130f79bd8425be5f1fd2f8a239c7e39e973b6b7f","abstract_canon_sha256":"df9b35f7ae73b632bfceb569a2658126e5dc1ddb1442c7ad8a65189455f86567"},"schema_version":"1.0"},"canonical_sha256":"5fe2f080e3598ae0345f058aafb21f2dee8dfc19d76895f384d9fb20064381c0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:29:07.667902Z","signature_b64":"VdfHaZ0aC76yFZbbhYataWzOHm2blC9+xP3hWTPTgf5ZQVHna6v/+nQ3PfkK2aB75Dz/uLg2C5OmpMlY0uw7Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5fe2f080e3598ae0345f058aafb21f2dee8dfc19d76895f384d9fb20064381c0","last_reissued_at":"2026-05-18T00:29:07.667118Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:29:07.667118Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1712.00003","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":"2026-05-18T00:29:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QVIbdzMleT8iiuzto7DIJNKotZJZes1TNf+e+QrrOxwZgG4MpnEvN1e6z4aygfpnqd3z2y6e5ueB+oBgqjSLDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-20T09:29:17.441370Z"},"content_sha256":"de77cbf435e9f388d4613746491dcb28f10ff26867fb0502e0e0535a6d72d321","schema_version":"1.0","event_id":"sha256:de77cbf435e9f388d4613746491dcb28f10ff26867fb0502e0e0535a6d72d321"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:L7RPBAHDLGFOANC7AWFK7MQ7FX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Modeling Information Flow Through Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Ahmad Chaddad, Behnaz Naisiri, Christian Desrosiers, Eric Granger, Marco Pedersoli, Matthew Toews","submitted_at":"2017-11-29T23:09:58Z","abstract_excerpt":"This paper proposes a principled information theoretic analysis of classification for deep neural network structures, e.g. convolutional neural networks (CNN). The output of convolutional filters is modeled as a random variable Y conditioned on the object class C and network filter bank F. The conditional entropy (CENT) H(Y |C,F) is shown in theory and experiments to be a highly compact and class-informative code, that can be computed from the filter outputs throughout an existing CNN and used to obtain higher classification results than the original CNN itself. Experiments demonstrate the eff"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.00003","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":""},"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":"2026-05-18T00:29:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/1PUZJgu3p1fZ8Bm+XNagKEU9EPQAHRu7XyiScoTyb6jEb3N0wQrcEZKOJnzaeNcuHTcCePgMfC+yNVBh3R/Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-20T09:29:17.442094Z"},"content_sha256":"c8a6c7c7a77740d5d73fb59e9cb311a4b091c1a9a5813c22fd81516206e779e9","schema_version":"1.0","event_id":"sha256:c8a6c7c7a77740d5d73fb59e9cb311a4b091c1a9a5813c22fd81516206e779e9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/L7RPBAHDLGFOANC7AWFK7MQ7FX/bundle.json","state_url":"https://pith.science/pith/L7RPBAHDLGFOANC7AWFK7MQ7FX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/L7RPBAHDLGFOANC7AWFK7MQ7FX/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":"2026-05-20T09:29:17Z","links":{"resolver":"https://pith.science/pith/L7RPBAHDLGFOANC7AWFK7MQ7FX","bundle":"https://pith.science/pith/L7RPBAHDLGFOANC7AWFK7MQ7FX/bundle.json","state":"https://pith.science/pith/L7RPBAHDLGFOANC7AWFK7MQ7FX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/L7RPBAHDLGFOANC7AWFK7MQ7FX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:L7RPBAHDLGFOANC7AWFK7MQ7FX","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":"df9b35f7ae73b632bfceb569a2658126e5dc1ddb1442c7ad8a65189455f86567","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-29T23:09:58Z","title_canon_sha256":"771446ad8bbd2f8cd59cd591130f79bd8425be5f1fd2f8a239c7e39e973b6b7f"},"schema_version":"1.0","source":{"id":"1712.00003","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.00003","created_at":"2026-05-18T00:29:07Z"},{"alias_kind":"arxiv_version","alias_value":"1712.00003v1","created_at":"2026-05-18T00:29:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.00003","created_at":"2026-05-18T00:29:07Z"},{"alias_kind":"pith_short_12","alias_value":"L7RPBAHDLGFO","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_16","alias_value":"L7RPBAHDLGFOANC7","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_8","alias_value":"L7RPBAHD","created_at":"2026-05-18T12:31:28Z"}],"graph_snapshots":[{"event_id":"sha256:c8a6c7c7a77740d5d73fb59e9cb311a4b091c1a9a5813c22fd81516206e779e9","target":"graph","created_at":"2026-05-18T00:29:07Z","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":"This paper proposes a principled information theoretic analysis of classification for deep neural network structures, e.g. convolutional neural networks (CNN). The output of convolutional filters is modeled as a random variable Y conditioned on the object class C and network filter bank F. The conditional entropy (CENT) H(Y |C,F) is shown in theory and experiments to be a highly compact and class-informative code, that can be computed from the filter outputs throughout an existing CNN and used to obtain higher classification results than the original CNN itself. Experiments demonstrate the eff","authors_text":"Ahmad Chaddad, Behnaz Naisiri, Christian Desrosiers, Eric Granger, Marco Pedersoli, Matthew Toews","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-29T23:09:58Z","title":"Modeling Information Flow Through Deep Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.00003","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:de77cbf435e9f388d4613746491dcb28f10ff26867fb0502e0e0535a6d72d321","target":"record","created_at":"2026-05-18T00:29:07Z","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":"df9b35f7ae73b632bfceb569a2658126e5dc1ddb1442c7ad8a65189455f86567","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-29T23:09:58Z","title_canon_sha256":"771446ad8bbd2f8cd59cd591130f79bd8425be5f1fd2f8a239c7e39e973b6b7f"},"schema_version":"1.0","source":{"id":"1712.00003","kind":"arxiv","version":1}},"canonical_sha256":"5fe2f080e3598ae0345f058aafb21f2dee8dfc19d76895f384d9fb20064381c0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5fe2f080e3598ae0345f058aafb21f2dee8dfc19d76895f384d9fb20064381c0","first_computed_at":"2026-05-18T00:29:07.667118Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:29:07.667118Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VdfHaZ0aC76yFZbbhYataWzOHm2blC9+xP3hWTPTgf5ZQVHna6v/+nQ3PfkK2aB75Dz/uLg2C5OmpMlY0uw7Ag==","signature_status":"signed_v1","signed_at":"2026-05-18T00:29:07.667902Z","signed_message":"canonical_sha256_bytes"},"source_id":"1712.00003","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:de77cbf435e9f388d4613746491dcb28f10ff26867fb0502e0e0535a6d72d321","sha256:c8a6c7c7a77740d5d73fb59e9cb311a4b091c1a9a5813c22fd81516206e779e9"],"state_sha256":"4fc15898c5c6230fe8fc754cb86de7fe13f7e1c379771f5a1982d537d05fc918"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IiLhwxHTJiPJ28WZet12Wf79E5qjYotf7XhOt7wC6xqOhMrChOvRoW80mxxNpRgzRTEW8tnKxmlUkipuKO12BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-20T09:29:17.445237Z","bundle_sha256":"388af6ac5fd96a69ba5ec96141e918aac5e93408504ac6e6f7a8f64d2e0b92ca"}}