{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:AOKWQVKZ6QKXMPO436RJTBS2NH","short_pith_number":"pith:AOKWQVKZ","canonical_record":{"source":{"id":"2008.08316","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-08-19T08:03:09Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"99bf1427125c004e1f9cf2e7d56a1b58281ed68abb32c71209de5396c034fc26","abstract_canon_sha256":"136bf2e6217a29146bdb9aabc15eadbec7115367c6bda6905c5ffdd0d2499191"},"schema_version":"1.0"},"canonical_sha256":"0395685559f415763ddcdfa299865a69f3779905070f8e9d11362da01bedf894","source":{"kind":"arxiv","id":"2008.08316","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2008.08316","created_at":"2026-07-05T01:28:32Z"},{"alias_kind":"arxiv_version","alias_value":"2008.08316v1","created_at":"2026-07-05T01:28:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2008.08316","created_at":"2026-07-05T01:28:32Z"},{"alias_kind":"pith_short_12","alias_value":"AOKWQVKZ6QKX","created_at":"2026-07-05T01:28:32Z"},{"alias_kind":"pith_short_16","alias_value":"AOKWQVKZ6QKXMPO4","created_at":"2026-07-05T01:28:32Z"},{"alias_kind":"pith_short_8","alias_value":"AOKWQVKZ","created_at":"2026-07-05T01:28:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:AOKWQVKZ6QKXMPO436RJTBS2NH","target":"record","payload":{"canonical_record":{"source":{"id":"2008.08316","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-08-19T08:03:09Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"99bf1427125c004e1f9cf2e7d56a1b58281ed68abb32c71209de5396c034fc26","abstract_canon_sha256":"136bf2e6217a29146bdb9aabc15eadbec7115367c6bda6905c5ffdd0d2499191"},"schema_version":"1.0"},"canonical_sha256":"0395685559f415763ddcdfa299865a69f3779905070f8e9d11362da01bedf894","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:28:32.074017Z","signature_b64":"v8iq2+R39unw8AKLnKXzo/21rh+Gu9BctOdZpZjqhEPWPsi+cYfItpa88vdWp86uv0cgapF9FwuGgluiHl2nAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0395685559f415763ddcdfa299865a69f3779905070f8e9d11362da01bedf894","last_reissued_at":"2026-07-05T01:28:32.073548Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:28:32.073548Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2008.08316","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":"2026-07-05T01:28:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/B6juFJxLzDFpgyVQf3ExYZpXBT+kvxeH0kQlAPAu7ISMPZ9QTquf2H+QXt1b4CqpuEpKs11N0urqoqDCmXTDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T15:56:26.611256Z"},"content_sha256":"a7975ddada4678e6365e51c73f09b85877234a62d3347582c8fdf7f710cbe638","schema_version":"1.0","event_id":"sha256:a7975ddada4678e6365e51c73f09b85877234a62d3347582c8fdf7f710cbe638"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:AOKWQVKZ6QKXMPO436RJTBS2NH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Data-Independent Structured Pruning of Neural Networks via Coresets","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Ben Mussay, Daniel Feldman, Margarita Osadchy, Samson Zhou, Vladimir Braverman","submitted_at":"2020-08-19T08:03:09Z","abstract_excerpt":"Model compression is crucial for deployment of neural networks on devices with limited computational and memory resources. Many different methods show comparable accuracy of the compressed model and similar compression rates. However, the majority of the compression methods are based on heuristics and offer no worst-case guarantees on the trade-off between the compression rate and the approximation error for an arbitrarily new sample. We propose the first efficient structured pruning algorithm with a provable trade-off between its compression rate and the approximation error for any future tes"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2008.08316","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2008.08316/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":"2026-07-05T01:28:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"26oIyyU9sSk98H3d6rJ2fR2etNHcb/Ba9H7KIJNzMmBGrlnF3g/wPoAGSK+GGDStB1Jwoy6yEprGO59sSRm2Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T15:56:26.611638Z"},"content_sha256":"0f2da8beddd8cac64ea12f584212f7d39a1b46586a8f00ed9e427582c1f11805","schema_version":"1.0","event_id":"sha256:0f2da8beddd8cac64ea12f584212f7d39a1b46586a8f00ed9e427582c1f11805"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AOKWQVKZ6QKXMPO436RJTBS2NH/bundle.json","state_url":"https://pith.science/pith/AOKWQVKZ6QKXMPO436RJTBS2NH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AOKWQVKZ6QKXMPO436RJTBS2NH/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":"2026-07-07T15:56:26Z","links":{"resolver":"https://pith.science/pith/AOKWQVKZ6QKXMPO436RJTBS2NH","bundle":"https://pith.science/pith/AOKWQVKZ6QKXMPO436RJTBS2NH/bundle.json","state":"https://pith.science/pith/AOKWQVKZ6QKXMPO436RJTBS2NH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AOKWQVKZ6QKXMPO436RJTBS2NH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:AOKWQVKZ6QKXMPO436RJTBS2NH","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":"136bf2e6217a29146bdb9aabc15eadbec7115367c6bda6905c5ffdd0d2499191","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-08-19T08:03:09Z","title_canon_sha256":"99bf1427125c004e1f9cf2e7d56a1b58281ed68abb32c71209de5396c034fc26"},"schema_version":"1.0","source":{"id":"2008.08316","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2008.08316","created_at":"2026-07-05T01:28:32Z"},{"alias_kind":"arxiv_version","alias_value":"2008.08316v1","created_at":"2026-07-05T01:28:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2008.08316","created_at":"2026-07-05T01:28:32Z"},{"alias_kind":"pith_short_12","alias_value":"AOKWQVKZ6QKX","created_at":"2026-07-05T01:28:32Z"},{"alias_kind":"pith_short_16","alias_value":"AOKWQVKZ6QKXMPO4","created_at":"2026-07-05T01:28:32Z"},{"alias_kind":"pith_short_8","alias_value":"AOKWQVKZ","created_at":"2026-07-05T01:28:32Z"}],"graph_snapshots":[{"event_id":"sha256:0f2da8beddd8cac64ea12f584212f7d39a1b46586a8f00ed9e427582c1f11805","target":"graph","created_at":"2026-07-05T01:28:32Z","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/2008.08316/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Model compression is crucial for deployment of neural networks on devices with limited computational and memory resources. Many different methods show comparable accuracy of the compressed model and similar compression rates. However, the majority of the compression methods are based on heuristics and offer no worst-case guarantees on the trade-off between the compression rate and the approximation error for an arbitrarily new sample. We propose the first efficient structured pruning algorithm with a provable trade-off between its compression rate and the approximation error for any future tes","authors_text":"Ben Mussay, Daniel Feldman, Margarita Osadchy, Samson Zhou, Vladimir Braverman","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-08-19T08:03:09Z","title":"Data-Independent Structured Pruning of Neural Networks via Coresets"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2008.08316","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:a7975ddada4678e6365e51c73f09b85877234a62d3347582c8fdf7f710cbe638","target":"record","created_at":"2026-07-05T01:28:32Z","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":"136bf2e6217a29146bdb9aabc15eadbec7115367c6bda6905c5ffdd0d2499191","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-08-19T08:03:09Z","title_canon_sha256":"99bf1427125c004e1f9cf2e7d56a1b58281ed68abb32c71209de5396c034fc26"},"schema_version":"1.0","source":{"id":"2008.08316","kind":"arxiv","version":1}},"canonical_sha256":"0395685559f415763ddcdfa299865a69f3779905070f8e9d11362da01bedf894","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0395685559f415763ddcdfa299865a69f3779905070f8e9d11362da01bedf894","first_computed_at":"2026-07-05T01:28:32.073548Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:28:32.073548Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"v8iq2+R39unw8AKLnKXzo/21rh+Gu9BctOdZpZjqhEPWPsi+cYfItpa88vdWp86uv0cgapF9FwuGgluiHl2nAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T01:28:32.074017Z","signed_message":"canonical_sha256_bytes"},"source_id":"2008.08316","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a7975ddada4678e6365e51c73f09b85877234a62d3347582c8fdf7f710cbe638","sha256:0f2da8beddd8cac64ea12f584212f7d39a1b46586a8f00ed9e427582c1f11805"],"state_sha256":"0b071401c21ce4353454d0f76c0e3a396c7f97ef16235398502130b911cd04ac"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5k4OGlCLOoooFFs/pNZDxfW5pxXGFNQc7slxr1afiJZup+xWfpxh74MjDpxchDITZaGfMjA9njBWPeNSw4alBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T15:56:26.613528Z","bundle_sha256":"0991271222d0b8f34e126e778ab4f64b88498048a20c275fbc8b580be5a1e12b"}}