{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:BBNGVDSPWAQB2D4N7VJUG4GKC7","short_pith_number":"pith:BBNGVDSP","canonical_record":{"source":{"id":"1812.07520","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-18T17:36:22Z","cross_cats_sorted":["cs.NE","stat.ML"],"title_canon_sha256":"92e94fa9daec40b8a095b818064eca2f25538bf836d3d3fe70282129638b8eeb","abstract_canon_sha256":"52b22e5c0d648e314464934d6c33345bfff5ee3afcc7a011ef036555ef34d67b"},"schema_version":"1.0"},"canonical_sha256":"085a6a8e4fb0201d0f8dfd534370ca17e43ef88656a63a2626cacb8dbfa6c22c","source":{"kind":"arxiv","id":"1812.07520","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.07520","created_at":"2026-05-17T23:57:56Z"},{"alias_kind":"arxiv_version","alias_value":"1812.07520v2","created_at":"2026-05-17T23:57:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.07520","created_at":"2026-05-17T23:57:56Z"},{"alias_kind":"pith_short_12","alias_value":"BBNGVDSPWAQB","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_16","alias_value":"BBNGVDSPWAQB2D4N","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_8","alias_value":"BBNGVDSP","created_at":"2026-05-18T12:32:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:BBNGVDSPWAQB2D4N7VJUG4GKC7","target":"record","payload":{"canonical_record":{"source":{"id":"1812.07520","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-18T17:36:22Z","cross_cats_sorted":["cs.NE","stat.ML"],"title_canon_sha256":"92e94fa9daec40b8a095b818064eca2f25538bf836d3d3fe70282129638b8eeb","abstract_canon_sha256":"52b22e5c0d648e314464934d6c33345bfff5ee3afcc7a011ef036555ef34d67b"},"schema_version":"1.0"},"canonical_sha256":"085a6a8e4fb0201d0f8dfd534370ca17e43ef88656a63a2626cacb8dbfa6c22c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:57:56.645990Z","signature_b64":"iWNRfdMNczvmCSCbZ2GVCEBS9I3tUj345ZmhXjHg6qTlzhskvSOKNJdeNFCZobuKePu+VmsqBGTRlWecINsMDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"085a6a8e4fb0201d0f8dfd534370ca17e43ef88656a63a2626cacb8dbfa6c22c","last_reissued_at":"2026-05-17T23:57:56.645375Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:57:56.645375Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1812.07520","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":"2026-05-17T23:57:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zjppcxqdzdPLtyP8X285YkvERn19/AkoBbz2XSxsb+6O29/ivQAiT/SKaorzBS91VxR3dJbHdJZiD62PbMPVAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T09:26:10.109652Z"},"content_sha256":"c13fc2148975b2a8e50dab03a523cff118b5a3add4a86ce847e3035fd002c8d2","schema_version":"1.0","event_id":"sha256:c13fc2148975b2a8e50dab03a523cff118b5a3add4a86ce847e3035fd002c8d2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:BBNGVDSPWAQB2D4N7VJUG4GKC7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Entropy-Constrained Training of Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE","stat.ML"],"primary_cat":"cs.LG","authors_text":"Arturo Marban, Klaus-Robert M\\\"uller, Simon Wiedemann, Wojciech Samek","submitted_at":"2018-12-18T17:36:22Z","abstract_excerpt":"We propose a general framework for neural network compression that is motivated by the Minimum Description Length (MDL) principle. For that we first derive an expression for the entropy of a neural network, which measures its complexity explicitly in terms of its bit-size. Then, we formalize the problem of neural network compression as an entropy-constrained optimization objective. This objective generalizes many of the compression techniques proposed in the literature, in that pruning or reducing the cardinality of the weight elements of the network can be seen special cases of entropy-minimi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.07520","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":"2026-05-17T23:57:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nqx0W5r6uYbbnXI5hXXX+adDWoGHwEdCeMshvkFPvWAkuoDaka6BYof0yNLQwRAgB6XH5SaA6HHcCVmP4TcYBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T09:26:10.110035Z"},"content_sha256":"7e65d9261754737fce4d4fe937a955a1f84e2306e8bdda81a535c29830701580","schema_version":"1.0","event_id":"sha256:7e65d9261754737fce4d4fe937a955a1f84e2306e8bdda81a535c29830701580"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BBNGVDSPWAQB2D4N7VJUG4GKC7/bundle.json","state_url":"https://pith.science/pith/BBNGVDSPWAQB2D4N7VJUG4GKC7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BBNGVDSPWAQB2D4N7VJUG4GKC7/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":"2026-05-25T09:26:10Z","links":{"resolver":"https://pith.science/pith/BBNGVDSPWAQB2D4N7VJUG4GKC7","bundle":"https://pith.science/pith/BBNGVDSPWAQB2D4N7VJUG4GKC7/bundle.json","state":"https://pith.science/pith/BBNGVDSPWAQB2D4N7VJUG4GKC7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BBNGVDSPWAQB2D4N7VJUG4GKC7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:BBNGVDSPWAQB2D4N7VJUG4GKC7","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":"52b22e5c0d648e314464934d6c33345bfff5ee3afcc7a011ef036555ef34d67b","cross_cats_sorted":["cs.NE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-18T17:36:22Z","title_canon_sha256":"92e94fa9daec40b8a095b818064eca2f25538bf836d3d3fe70282129638b8eeb"},"schema_version":"1.0","source":{"id":"1812.07520","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.07520","created_at":"2026-05-17T23:57:56Z"},{"alias_kind":"arxiv_version","alias_value":"1812.07520v2","created_at":"2026-05-17T23:57:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.07520","created_at":"2026-05-17T23:57:56Z"},{"alias_kind":"pith_short_12","alias_value":"BBNGVDSPWAQB","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_16","alias_value":"BBNGVDSPWAQB2D4N","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_8","alias_value":"BBNGVDSP","created_at":"2026-05-18T12:32:13Z"}],"graph_snapshots":[{"event_id":"sha256:7e65d9261754737fce4d4fe937a955a1f84e2306e8bdda81a535c29830701580","target":"graph","created_at":"2026-05-17T23:57:56Z","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":"We propose a general framework for neural network compression that is motivated by the Minimum Description Length (MDL) principle. For that we first derive an expression for the entropy of a neural network, which measures its complexity explicitly in terms of its bit-size. Then, we formalize the problem of neural network compression as an entropy-constrained optimization objective. This objective generalizes many of the compression techniques proposed in the literature, in that pruning or reducing the cardinality of the weight elements of the network can be seen special cases of entropy-minimi","authors_text":"Arturo Marban, Klaus-Robert M\\\"uller, Simon Wiedemann, Wojciech Samek","cross_cats":["cs.NE","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-18T17:36:22Z","title":"Entropy-Constrained Training of Deep Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.07520","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:c13fc2148975b2a8e50dab03a523cff118b5a3add4a86ce847e3035fd002c8d2","target":"record","created_at":"2026-05-17T23:57:56Z","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":"52b22e5c0d648e314464934d6c33345bfff5ee3afcc7a011ef036555ef34d67b","cross_cats_sorted":["cs.NE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-18T17:36:22Z","title_canon_sha256":"92e94fa9daec40b8a095b818064eca2f25538bf836d3d3fe70282129638b8eeb"},"schema_version":"1.0","source":{"id":"1812.07520","kind":"arxiv","version":2}},"canonical_sha256":"085a6a8e4fb0201d0f8dfd534370ca17e43ef88656a63a2626cacb8dbfa6c22c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"085a6a8e4fb0201d0f8dfd534370ca17e43ef88656a63a2626cacb8dbfa6c22c","first_computed_at":"2026-05-17T23:57:56.645375Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:57:56.645375Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"iWNRfdMNczvmCSCbZ2GVCEBS9I3tUj345ZmhXjHg6qTlzhskvSOKNJdeNFCZobuKePu+VmsqBGTRlWecINsMDg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:57:56.645990Z","signed_message":"canonical_sha256_bytes"},"source_id":"1812.07520","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c13fc2148975b2a8e50dab03a523cff118b5a3add4a86ce847e3035fd002c8d2","sha256:7e65d9261754737fce4d4fe937a955a1f84e2306e8bdda81a535c29830701580"],"state_sha256":"1425fa0d487cc7d10e50d1629155773fd5721ab65d8937066bb41249d3d89f24"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yMOQSCWZUHJXrjBVuBmzIq1rI0jANcVhycWUazz7RFnALwDKIgUg2rzVUpGm/Fj1hW1ZsJ3sdUp+tvunw2BWAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T09:26:10.113098Z","bundle_sha256":"8ce8c8c0b9bc950a3740be1adaa6cb360b17b45a8db3c77660ee40ec3239d6d9"}}