{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:264L5HENAUIXMJO6XITKJG6HB4","short_pith_number":"pith:264L5HEN","canonical_record":{"source":{"id":"1904.10873","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-04-24T15:31:55Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"e56b53c5f5a40a07d409c723a799d1d92434f60e714b8adcaf597c6af88d7828","abstract_canon_sha256":"2d3255cf9e7d63ac12f6036966c604c7a6f3e727efaade26dbcc3c6ae0a7428f"},"schema_version":"1.0"},"canonical_sha256":"d7b8be9c8d05117625deba26a49bc70f30b779b36870924d1eb2e964cc0812b7","source":{"kind":"arxiv","id":"1904.10873","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.10873","created_at":"2026-05-17T23:47:49Z"},{"alias_kind":"arxiv_version","alias_value":"1904.10873v1","created_at":"2026-05-17T23:47:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.10873","created_at":"2026-05-17T23:47:49Z"},{"alias_kind":"pith_short_12","alias_value":"264L5HENAUIX","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_16","alias_value":"264L5HENAUIXMJO6","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_8","alias_value":"264L5HEN","created_at":"2026-05-18T12:33:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:264L5HENAUIXMJO6XITKJG6HB4","target":"record","payload":{"canonical_record":{"source":{"id":"1904.10873","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-04-24T15:31:55Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"e56b53c5f5a40a07d409c723a799d1d92434f60e714b8adcaf597c6af88d7828","abstract_canon_sha256":"2d3255cf9e7d63ac12f6036966c604c7a6f3e727efaade26dbcc3c6ae0a7428f"},"schema_version":"1.0"},"canonical_sha256":"d7b8be9c8d05117625deba26a49bc70f30b779b36870924d1eb2e964cc0812b7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:47:49.841860Z","signature_b64":"kRBCJA/XxMGTiyuZJQX6BdObSqgwzh8c0L+jYiPQXR1JrvCoxbTYXv+B7/T86N9a1jl6uFkCnEiaJddQeh5qCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d7b8be9c8d05117625deba26a49bc70f30b779b36870924d1eb2e964cc0812b7","last_reissued_at":"2026-05-17T23:47:49.841331Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:47:49.841331Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1904.10873","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":"2026-05-17T23:47:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"e4I+dQU7kDapHzdtMczKCsJgFIbwyJpKeVb2M8yseqBoyjzM2rEAp/G8koNgOJS8HGnwu/pRYkPDMehuRnYIBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T03:58:06.367925Z"},"content_sha256":"d1f0788431934c85181a725ebc3d10959dc625fa33ba7fb6e4bec74e41af2334","schema_version":"1.0","event_id":"sha256:d1f0788431934c85181a725ebc3d10959dc625fa33ba7fb6e4bec74e41af2334"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:264L5HENAUIXMJO6XITKJG6HB4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"$S^{2}$-LBI: Stochastic Split Linearized Bregman Iterations for Parsimonious Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"stat.ML","authors_text":"Donghao Li, Shun Zhang, Xinwei Sun, Yanwei Fu, Yizhou Wang, Yuan Yao","submitted_at":"2019-04-24T15:31:55Z","abstract_excerpt":"This paper proposes a novel Stochastic Split Linearized Bregman Iteration ($S^{2}$-LBI) algorithm to efficiently train the deep network. The $S^{2}$-LBI introduces an iterative regularization path with structural sparsity. Our $S^{2}$-LBI combines the computational efficiency of the LBI, and model selection consistency in learning the structural sparsity. The computed solution path intrinsically enables us to enlarge or simplify a network, which theoretically, is benefited from the dynamics property of our $S^{2}$-LBI algorithm. The experimental results validate our $S^{2}$-LBI on MNIST and CI"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.10873","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":"2026-05-17T23:47:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9StxB1GQBDo9vNCcWDiEycOUhjjtqXC/Aa2LK+Grxw43264L+ehzSBoVCmHb7675fHVJ8gQ6Ipg3BG3AV7RBBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T03:58:06.368584Z"},"content_sha256":"8a585436cca1c6b75b37e6f33e29eb652db1284a260b67eb3e300aa5ff50ddbf","schema_version":"1.0","event_id":"sha256:8a585436cca1c6b75b37e6f33e29eb652db1284a260b67eb3e300aa5ff50ddbf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/264L5HENAUIXMJO6XITKJG6HB4/bundle.json","state_url":"https://pith.science/pith/264L5HENAUIXMJO6XITKJG6HB4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/264L5HENAUIXMJO6XITKJG6HB4/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":"2026-05-26T03:58:06Z","links":{"resolver":"https://pith.science/pith/264L5HENAUIXMJO6XITKJG6HB4","bundle":"https://pith.science/pith/264L5HENAUIXMJO6XITKJG6HB4/bundle.json","state":"https://pith.science/pith/264L5HENAUIXMJO6XITKJG6HB4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/264L5HENAUIXMJO6XITKJG6HB4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:264L5HENAUIXMJO6XITKJG6HB4","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":"2d3255cf9e7d63ac12f6036966c604c7a6f3e727efaade26dbcc3c6ae0a7428f","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-04-24T15:31:55Z","title_canon_sha256":"e56b53c5f5a40a07d409c723a799d1d92434f60e714b8adcaf597c6af88d7828"},"schema_version":"1.0","source":{"id":"1904.10873","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.10873","created_at":"2026-05-17T23:47:49Z"},{"alias_kind":"arxiv_version","alias_value":"1904.10873v1","created_at":"2026-05-17T23:47:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.10873","created_at":"2026-05-17T23:47:49Z"},{"alias_kind":"pith_short_12","alias_value":"264L5HENAUIX","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_16","alias_value":"264L5HENAUIXMJO6","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_8","alias_value":"264L5HEN","created_at":"2026-05-18T12:33:07Z"}],"graph_snapshots":[{"event_id":"sha256:8a585436cca1c6b75b37e6f33e29eb652db1284a260b67eb3e300aa5ff50ddbf","target":"graph","created_at":"2026-05-17T23:47:49Z","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 novel Stochastic Split Linearized Bregman Iteration ($S^{2}$-LBI) algorithm to efficiently train the deep network. The $S^{2}$-LBI introduces an iterative regularization path with structural sparsity. Our $S^{2}$-LBI combines the computational efficiency of the LBI, and model selection consistency in learning the structural sparsity. The computed solution path intrinsically enables us to enlarge or simplify a network, which theoretically, is benefited from the dynamics property of our $S^{2}$-LBI algorithm. The experimental results validate our $S^{2}$-LBI on MNIST and CI","authors_text":"Donghao Li, Shun Zhang, Xinwei Sun, Yanwei Fu, Yizhou Wang, Yuan Yao","cross_cats":["cs.CV","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-04-24T15:31:55Z","title":"$S^{2}$-LBI: Stochastic Split Linearized Bregman Iterations for Parsimonious Deep Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.10873","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:d1f0788431934c85181a725ebc3d10959dc625fa33ba7fb6e4bec74e41af2334","target":"record","created_at":"2026-05-17T23:47:49Z","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":"2d3255cf9e7d63ac12f6036966c604c7a6f3e727efaade26dbcc3c6ae0a7428f","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-04-24T15:31:55Z","title_canon_sha256":"e56b53c5f5a40a07d409c723a799d1d92434f60e714b8adcaf597c6af88d7828"},"schema_version":"1.0","source":{"id":"1904.10873","kind":"arxiv","version":1}},"canonical_sha256":"d7b8be9c8d05117625deba26a49bc70f30b779b36870924d1eb2e964cc0812b7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d7b8be9c8d05117625deba26a49bc70f30b779b36870924d1eb2e964cc0812b7","first_computed_at":"2026-05-17T23:47:49.841331Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:47:49.841331Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kRBCJA/XxMGTiyuZJQX6BdObSqgwzh8c0L+jYiPQXR1JrvCoxbTYXv+B7/T86N9a1jl6uFkCnEiaJddQeh5qCQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:47:49.841860Z","signed_message":"canonical_sha256_bytes"},"source_id":"1904.10873","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d1f0788431934c85181a725ebc3d10959dc625fa33ba7fb6e4bec74e41af2334","sha256:8a585436cca1c6b75b37e6f33e29eb652db1284a260b67eb3e300aa5ff50ddbf"],"state_sha256":"e0a0c0d241b7582de34ebfdb03e43eb01c383db5874a53408bd73cddfd55d534"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BmlrOq6AWJ6YvzrqF/tX1OaEQe1T2y95NRGfrBEV+yydD6f9IRYZIK0pirMa/LxfGXv3F4L+fVCTUcEb2114AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T03:58:06.371780Z","bundle_sha256":"ca174e3696dd9bc54a9eea2eae07a6f83cb64c3974ee5edbdd5a97d18cc4a593"}}