{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:5MXV2K7S7VNHNCAZJQHQGLSIJF","short_pith_number":"pith:5MXV2K7S","canonical_record":{"source":{"id":"1705.07565","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-05-22T05:54:37Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"93c57d2839635eff68e5bda2032694b47c872bdf82578263cd4c41172f89c286","abstract_canon_sha256":"c4cf99a4bf898fd657ff76fa5df5ca5450b715e525adc38699e6f29f97b882b0"},"schema_version":"1.0"},"canonical_sha256":"eb2f5d2bf2fd5a7688194c0f032e484955e576bf1c3d45140c5d48dc51dd4ed5","source":{"kind":"arxiv","id":"1705.07565","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.07565","created_at":"2026-07-05T00:08:15Z"},{"alias_kind":"arxiv_version","alias_value":"1705.07565v2","created_at":"2026-07-05T00:08:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.07565","created_at":"2026-07-05T00:08:15Z"},{"alias_kind":"pith_short_12","alias_value":"5MXV2K7S7VNH","created_at":"2026-07-05T00:08:15Z"},{"alias_kind":"pith_short_16","alias_value":"5MXV2K7S7VNHNCAZ","created_at":"2026-07-05T00:08:15Z"},{"alias_kind":"pith_short_8","alias_value":"5MXV2K7S","created_at":"2026-07-05T00:08:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:5MXV2K7S7VNHNCAZJQHQGLSIJF","target":"record","payload":{"canonical_record":{"source":{"id":"1705.07565","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-05-22T05:54:37Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"93c57d2839635eff68e5bda2032694b47c872bdf82578263cd4c41172f89c286","abstract_canon_sha256":"c4cf99a4bf898fd657ff76fa5df5ca5450b715e525adc38699e6f29f97b882b0"},"schema_version":"1.0"},"canonical_sha256":"eb2f5d2bf2fd5a7688194c0f032e484955e576bf1c3d45140c5d48dc51dd4ed5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:08:15.638980Z","signature_b64":"+lD6zXn078+8eEiGBiFRYAmZlRaGEHHskpSSs2em8ee+tbLak00RcXO/2rHKMvBHn3FC2oXoS8iYXQoYht6gBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eb2f5d2bf2fd5a7688194c0f032e484955e576bf1c3d45140c5d48dc51dd4ed5","last_reissued_at":"2026-07-05T00:08:15.638576Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:08:15.638576Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1705.07565","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":"2026-07-05T00:08:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"e/E+gg+CRpGjdLaf5O2AUCByI80A+oi791rPiq15++SxuhjCgWAoykb8gX4u7YpBIWNm6km/kDI3oIBjRnO9DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T20:58:02.039038Z"},"content_sha256":"7a1b3cfdf9449420eed34a6d37370abe33af2399b1b1e05853eb058911f3f681","schema_version":"1.0","event_id":"sha256:7a1b3cfdf9449420eed34a6d37370abe33af2399b1b1e05853eb058911f3f681"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:5MXV2K7S7VNHNCAZJQHQGLSIJF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.NE","authors_text":"Shangyu Chen, Sinno Jialin Pan, Xin Dong","submitted_at":"2017-05-22T05:54:37Z","abstract_excerpt":"How to develop slim and accurate deep neural networks has become crucial for real- world applications, especially for those employed in embedded systems. Though previous work along this research line has shown some promising results, most existing methods either fail to significantly compress a well-trained deep network or require a heavy retraining process for the pruned deep network to re-boost its prediction performance. In this paper, we propose a new layer-wise pruning method for deep neural networks. In our proposed method, parameters of each individual layer are pruned independently bas"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.07565","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/1705.07565/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":"2026-07-05T00:08:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HCv4UCIBm4+19nQtRAY6LXe6Y5X7gIqikSUAUDcGzHSrxwJwNDVs17Xy7Ni8xd7zYrnpE+F5dgAZ2fVRFnmZBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T20:58:02.039576Z"},"content_sha256":"49571d20b8eafeae7421989e37f046a24f9d337791843c7dbbf68c0f90ac69f3","schema_version":"1.0","event_id":"sha256:49571d20b8eafeae7421989e37f046a24f9d337791843c7dbbf68c0f90ac69f3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5MXV2K7S7VNHNCAZJQHQGLSIJF/bundle.json","state_url":"https://pith.science/pith/5MXV2K7S7VNHNCAZJQHQGLSIJF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5MXV2K7S7VNHNCAZJQHQGLSIJF/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":"2026-08-09T20:58:02Z","links":{"resolver":"https://pith.science/pith/5MXV2K7S7VNHNCAZJQHQGLSIJF","bundle":"https://pith.science/pith/5MXV2K7S7VNHNCAZJQHQGLSIJF/bundle.json","state":"https://pith.science/pith/5MXV2K7S7VNHNCAZJQHQGLSIJF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5MXV2K7S7VNHNCAZJQHQGLSIJF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:5MXV2K7S7VNHNCAZJQHQGLSIJF","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":"c4cf99a4bf898fd657ff76fa5df5ca5450b715e525adc38699e6f29f97b882b0","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-05-22T05:54:37Z","title_canon_sha256":"93c57d2839635eff68e5bda2032694b47c872bdf82578263cd4c41172f89c286"},"schema_version":"1.0","source":{"id":"1705.07565","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.07565","created_at":"2026-07-05T00:08:15Z"},{"alias_kind":"arxiv_version","alias_value":"1705.07565v2","created_at":"2026-07-05T00:08:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.07565","created_at":"2026-07-05T00:08:15Z"},{"alias_kind":"pith_short_12","alias_value":"5MXV2K7S7VNH","created_at":"2026-07-05T00:08:15Z"},{"alias_kind":"pith_short_16","alias_value":"5MXV2K7S7VNHNCAZ","created_at":"2026-07-05T00:08:15Z"},{"alias_kind":"pith_short_8","alias_value":"5MXV2K7S","created_at":"2026-07-05T00:08:15Z"}],"graph_snapshots":[{"event_id":"sha256:49571d20b8eafeae7421989e37f046a24f9d337791843c7dbbf68c0f90ac69f3","target":"graph","created_at":"2026-07-05T00:08:15Z","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/1705.07565/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"How to develop slim and accurate deep neural networks has become crucial for real- world applications, especially for those employed in embedded systems. Though previous work along this research line has shown some promising results, most existing methods either fail to significantly compress a well-trained deep network or require a heavy retraining process for the pruned deep network to re-boost its prediction performance. In this paper, we propose a new layer-wise pruning method for deep neural networks. In our proposed method, parameters of each individual layer are pruned independently bas","authors_text":"Shangyu Chen, Sinno Jialin Pan, Xin Dong","cross_cats":["cs.CV","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-05-22T05:54:37Z","title":"Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.07565","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":"sha256:7a1b3cfdf9449420eed34a6d37370abe33af2399b1b1e05853eb058911f3f681","target":"record","created_at":"2026-07-05T00:08:15Z","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":"c4cf99a4bf898fd657ff76fa5df5ca5450b715e525adc38699e6f29f97b882b0","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-05-22T05:54:37Z","title_canon_sha256":"93c57d2839635eff68e5bda2032694b47c872bdf82578263cd4c41172f89c286"},"schema_version":"1.0","source":{"id":"1705.07565","kind":"arxiv","version":2}},"canonical_sha256":"eb2f5d2bf2fd5a7688194c0f032e484955e576bf1c3d45140c5d48dc51dd4ed5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"eb2f5d2bf2fd5a7688194c0f032e484955e576bf1c3d45140c5d48dc51dd4ed5","first_computed_at":"2026-07-05T00:08:15.638576Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:08:15.638576Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+lD6zXn078+8eEiGBiFRYAmZlRaGEHHskpSSs2em8ee+tbLak00RcXO/2rHKMvBHn3FC2oXoS8iYXQoYht6gBg==","signature_status":"signed_v1","signed_at":"2026-07-05T00:08:15.638980Z","signed_message":"canonical_sha256_bytes"},"source_id":"1705.07565","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7a1b3cfdf9449420eed34a6d37370abe33af2399b1b1e05853eb058911f3f681","sha256:49571d20b8eafeae7421989e37f046a24f9d337791843c7dbbf68c0f90ac69f3"],"state_sha256":"e69657235fdc705cf526963e45571b3cf3b29ce7f51ae84c2cd11e38ccc621c5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vLMqdCbjFfiAuofyziyThxeE/9u5rDl7/mI4VDfoMGsbxr4E1JaxE2XOj5Fz9QkyhiUjV8bzkYXlrbd46wFyCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T20:58:02.046716Z","bundle_sha256":"1c1808295b7a28922b300e8981a2017cf16b48f9c81cc7ff3ea1d56c1a6d1db4"}}