{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:4KGZPLT4UGRKKMAOSJV5JNTKZD","short_pith_number":"pith:4KGZPLT4","canonical_record":{"source":{"id":"1905.01044","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-03T06:46:37Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"688928f7f3dc229c238ad3236251d753ab3ac20c2c515d020ed9692e8c351332","abstract_canon_sha256":"e06e7266aa27f9229c5cf28de8b4ac86c444ab5c1790b249a91cfd6654868006"},"schema_version":"1.0"},"canonical_sha256":"e28d97ae7ca1a2a5300e926bd4b66ac8d6d4e1eb6c05600a60fbb833bb292169","source":{"kind":"arxiv","id":"1905.01044","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.01044","created_at":"2026-05-17T23:47:07Z"},{"alias_kind":"arxiv_version","alias_value":"1905.01044v1","created_at":"2026-05-17T23:47:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.01044","created_at":"2026-05-17T23:47:07Z"},{"alias_kind":"pith_short_12","alias_value":"4KGZPLT4UGRK","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"4KGZPLT4UGRKKMAO","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"4KGZPLT4","created_at":"2026-05-18T12:33:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:4KGZPLT4UGRKKMAOSJV5JNTKZD","target":"record","payload":{"canonical_record":{"source":{"id":"1905.01044","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-03T06:46:37Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"688928f7f3dc229c238ad3236251d753ab3ac20c2c515d020ed9692e8c351332","abstract_canon_sha256":"e06e7266aa27f9229c5cf28de8b4ac86c444ab5c1790b249a91cfd6654868006"},"schema_version":"1.0"},"canonical_sha256":"e28d97ae7ca1a2a5300e926bd4b66ac8d6d4e1eb6c05600a60fbb833bb292169","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:47:07.776416Z","signature_b64":"EKHQZJ3G/OwXyi75w1CIV44gmXbYrmD9GqhoN7wT++UClaVXFeuORB+y6wnp4pQC1GiIJLL0mgDd1K1NKjiIDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e28d97ae7ca1a2a5300e926bd4b66ac8d6d4e1eb6c05600a60fbb833bb292169","last_reissued_at":"2026-05-17T23:47:07.775657Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:47:07.775657Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.01044","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":"2026-05-17T23:47:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SN/Gc34aSaQ7o4/4FNGJRtj3pE8eauIhbYCxFMkeBuOC+fPuHgdq/ISRf3N7qszibmu+hCY7uIXlTKhFopHGDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T23:14:16.627989Z"},"content_sha256":"86074461bccdc6c8292000c2a10c1582ed54a151fade7a5f31e4f8286d7d6e5b","schema_version":"1.0","event_id":"sha256:86074461bccdc6c8292000c2a10c1582ed54a151fade7a5f31e4f8286d7d6e5b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:4KGZPLT4UGRKKMAOSJV5JNTKZD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Compressibility Loss for Neural Network Weights","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Caglar Aytekin, Emre Aksu, Francesco Cricri","submitted_at":"2019-05-03T06:46:37Z","abstract_excerpt":"In this paper we apply a compressibility loss that enables learning highly compressible neural network weights. The loss was previously proposed as a measure of negated sparsity of a signal, yet in this paper we show that minimizing this loss also enforces the non-zero parts of the signal to have very low entropy, thus making the entire signal more compressible. For an optimization problem where the goal is to minimize the compressibility loss (the objective), we prove that at any critical point of the objective, the weight vector is a ternary signal and the corresponding value of the objectiv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.01044","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":"2026-05-17T23:47:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+UnCcYPnKxYo7zikBJgys19Iwj1qS6TMllVlGHymBNTE2w6FeU+q77TkwZaTCiz6ClovghwvzrXFSmOB7MgyBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T23:14:16.628345Z"},"content_sha256":"0d29c2c5a2454e3ec8a31bd311fecea8956681c26347e712657a9b174537b4db","schema_version":"1.0","event_id":"sha256:0d29c2c5a2454e3ec8a31bd311fecea8956681c26347e712657a9b174537b4db"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4KGZPLT4UGRKKMAOSJV5JNTKZD/bundle.json","state_url":"https://pith.science/pith/4KGZPLT4UGRKKMAOSJV5JNTKZD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4KGZPLT4UGRKKMAOSJV5JNTKZD/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":"2026-07-04T23:14:16Z","links":{"resolver":"https://pith.science/pith/4KGZPLT4UGRKKMAOSJV5JNTKZD","bundle":"https://pith.science/pith/4KGZPLT4UGRKKMAOSJV5JNTKZD/bundle.json","state":"https://pith.science/pith/4KGZPLT4UGRKKMAOSJV5JNTKZD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4KGZPLT4UGRKKMAOSJV5JNTKZD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:4KGZPLT4UGRKKMAOSJV5JNTKZD","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":"e06e7266aa27f9229c5cf28de8b4ac86c444ab5c1790b249a91cfd6654868006","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-03T06:46:37Z","title_canon_sha256":"688928f7f3dc229c238ad3236251d753ab3ac20c2c515d020ed9692e8c351332"},"schema_version":"1.0","source":{"id":"1905.01044","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.01044","created_at":"2026-05-17T23:47:07Z"},{"alias_kind":"arxiv_version","alias_value":"1905.01044v1","created_at":"2026-05-17T23:47:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.01044","created_at":"2026-05-17T23:47:07Z"},{"alias_kind":"pith_short_12","alias_value":"4KGZPLT4UGRK","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"4KGZPLT4UGRKKMAO","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"4KGZPLT4","created_at":"2026-05-18T12:33:10Z"}],"graph_snapshots":[{"event_id":"sha256:0d29c2c5a2454e3ec8a31bd311fecea8956681c26347e712657a9b174537b4db","target":"graph","created_at":"2026-05-17T23:47:07Z","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":"In this paper we apply a compressibility loss that enables learning highly compressible neural network weights. The loss was previously proposed as a measure of negated sparsity of a signal, yet in this paper we show that minimizing this loss also enforces the non-zero parts of the signal to have very low entropy, thus making the entire signal more compressible. For an optimization problem where the goal is to minimize the compressibility loss (the objective), we prove that at any critical point of the objective, the weight vector is a ternary signal and the corresponding value of the objectiv","authors_text":"Caglar Aytekin, Emre Aksu, Francesco Cricri","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-03T06:46:37Z","title":"Compressibility Loss for Neural Network Weights"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.01044","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":"sha256:86074461bccdc6c8292000c2a10c1582ed54a151fade7a5f31e4f8286d7d6e5b","target":"record","created_at":"2026-05-17T23:47:07Z","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":"e06e7266aa27f9229c5cf28de8b4ac86c444ab5c1790b249a91cfd6654868006","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-03T06:46:37Z","title_canon_sha256":"688928f7f3dc229c238ad3236251d753ab3ac20c2c515d020ed9692e8c351332"},"schema_version":"1.0","source":{"id":"1905.01044","kind":"arxiv","version":1}},"canonical_sha256":"e28d97ae7ca1a2a5300e926bd4b66ac8d6d4e1eb6c05600a60fbb833bb292169","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e28d97ae7ca1a2a5300e926bd4b66ac8d6d4e1eb6c05600a60fbb833bb292169","first_computed_at":"2026-05-17T23:47:07.775657Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:47:07.775657Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EKHQZJ3G/OwXyi75w1CIV44gmXbYrmD9GqhoN7wT++UClaVXFeuORB+y6wnp4pQC1GiIJLL0mgDd1K1NKjiIDw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:47:07.776416Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.01044","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:86074461bccdc6c8292000c2a10c1582ed54a151fade7a5f31e4f8286d7d6e5b","sha256:0d29c2c5a2454e3ec8a31bd311fecea8956681c26347e712657a9b174537b4db"],"state_sha256":"9aa4a270da83ef83369bc61358cdc52b0c4b86c1073dd1938256f5f06a3733a2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9dTGVW//bP5GyrEDzrRE0atS5/ooQBiAX66js6ytgOXrq6gjSpNP7ETNwFRkrrYvloLFT81pRmU9fNRBk04/AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-04T23:14:16.630247Z","bundle_sha256":"4c8827a668bf49dbad50ef9500a9001ca0ba49e197f1784cbdf8ba22f81cec0f"}}