{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:XAM5IXLXBRDP6R3M5TTP3R5O7F","short_pith_number":"pith:XAM5IXLX","canonical_record":{"source":{"id":"2401.03619","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-01-08T01:22:00Z","cross_cats_sorted":["cs.SY","eess.SY"],"title_canon_sha256":"06c6dcdac96b6f95246bbb32f30141e10d1ef9812341029508ae4dea1672e687","abstract_canon_sha256":"781865ea04a0cd3f0ac5c9b50ee259b215bfc070dd56aa39ae458395cbcabe02"},"schema_version":"1.0"},"canonical_sha256":"b819d45d770c46ff476cece6fdc7aef9556ec0da03748a0707fabec848284946","source":{"kind":"arxiv","id":"2401.03619","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.03619","created_at":"2026-07-05T07:31:11Z"},{"alias_kind":"arxiv_version","alias_value":"2401.03619v1","created_at":"2026-07-05T07:31:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.03619","created_at":"2026-07-05T07:31:11Z"},{"alias_kind":"pith_short_12","alias_value":"XAM5IXLXBRDP","created_at":"2026-07-05T07:31:11Z"},{"alias_kind":"pith_short_16","alias_value":"XAM5IXLXBRDP6R3M","created_at":"2026-07-05T07:31:11Z"},{"alias_kind":"pith_short_8","alias_value":"XAM5IXLX","created_at":"2026-07-05T07:31:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:XAM5IXLXBRDP6R3M5TTP3R5O7F","target":"record","payload":{"canonical_record":{"source":{"id":"2401.03619","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-01-08T01:22:00Z","cross_cats_sorted":["cs.SY","eess.SY"],"title_canon_sha256":"06c6dcdac96b6f95246bbb32f30141e10d1ef9812341029508ae4dea1672e687","abstract_canon_sha256":"781865ea04a0cd3f0ac5c9b50ee259b215bfc070dd56aa39ae458395cbcabe02"},"schema_version":"1.0"},"canonical_sha256":"b819d45d770c46ff476cece6fdc7aef9556ec0da03748a0707fabec848284946","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:31:11.665404Z","signature_b64":"+YoIaZTwXRjRrsxc9xU9ctwOzbUb15ArrTUxF9Q3JDIIrmBZRRyPHvuAERL3tOlrE6fcmp+YawmRIWy82MjHBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b819d45d770c46ff476cece6fdc7aef9556ec0da03748a0707fabec848284946","last_reissued_at":"2026-07-05T07:31:11.664906Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:31:11.664906Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2401.03619","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":"2026-07-05T07:31:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/EQDJP8bHT2dqGudEeETqDpgXBmuvsXXNsAz1bbQ3xtsV4odUM/9VWd4euk60A2+MbC7Cbh3C6aSrL0jIUhSDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T17:13:17.776349Z"},"content_sha256":"eea17cba07caa9fb1cebd7201497e1779f0fa7752efcf0fb9615fdb236d6ac35","schema_version":"1.0","event_id":"sha256:eea17cba07caa9fb1cebd7201497e1779f0fa7752efcf0fb9615fdb236d6ac35"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:XAM5IXLXBRDP6R3M5TTP3R5O7F","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"AA-DLADMM: An Accelerated ADMM-based Framework for Training Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Gustavo Batista, Mohammad Deghat, Zeinab Ebrahimi","submitted_at":"2024-01-08T01:22:00Z","abstract_excerpt":"Stochastic gradient descent (SGD) and its many variants are the widespread optimization algorithms for training deep neural networks. However, SGD suffers from inevitable drawbacks, including vanishing gradients, lack of theoretical guarantees, and substantial sensitivity to input. The Alternating Direction Method of Multipliers (ADMM) has been proposed to address these shortcomings as an effective alternative to the gradient-based methods. It has been successfully employed for training deep neural networks. However, ADMM-based optimizers have a slow convergence rate. This paper proposes an An"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.03619","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/2401.03619/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":"2026-07-05T07:31:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kQTs/iZt23SCFX/lmOwiHSLpPSixyxShETi/iMPm2I2hpfqJJYHOQPLJhw/9DYOcYEbA+QXpDs2Tz5kxBo4ECg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T17:13:17.776745Z"},"content_sha256":"ef466607fbdedf66277885510a19260782438de4f2114cd282bf3467a9473bc0","schema_version":"1.0","event_id":"sha256:ef466607fbdedf66277885510a19260782438de4f2114cd282bf3467a9473bc0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XAM5IXLXBRDP6R3M5TTP3R5O7F/bundle.json","state_url":"https://pith.science/pith/XAM5IXLXBRDP6R3M5TTP3R5O7F/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XAM5IXLXBRDP6R3M5TTP3R5O7F/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":"2026-08-05T17:13:17Z","links":{"resolver":"https://pith.science/pith/XAM5IXLXBRDP6R3M5TTP3R5O7F","bundle":"https://pith.science/pith/XAM5IXLXBRDP6R3M5TTP3R5O7F/bundle.json","state":"https://pith.science/pith/XAM5IXLXBRDP6R3M5TTP3R5O7F/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XAM5IXLXBRDP6R3M5TTP3R5O7F/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:XAM5IXLXBRDP6R3M5TTP3R5O7F","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":"781865ea04a0cd3f0ac5c9b50ee259b215bfc070dd56aa39ae458395cbcabe02","cross_cats_sorted":["cs.SY","eess.SY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-01-08T01:22:00Z","title_canon_sha256":"06c6dcdac96b6f95246bbb32f30141e10d1ef9812341029508ae4dea1672e687"},"schema_version":"1.0","source":{"id":"2401.03619","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.03619","created_at":"2026-07-05T07:31:11Z"},{"alias_kind":"arxiv_version","alias_value":"2401.03619v1","created_at":"2026-07-05T07:31:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.03619","created_at":"2026-07-05T07:31:11Z"},{"alias_kind":"pith_short_12","alias_value":"XAM5IXLXBRDP","created_at":"2026-07-05T07:31:11Z"},{"alias_kind":"pith_short_16","alias_value":"XAM5IXLXBRDP6R3M","created_at":"2026-07-05T07:31:11Z"},{"alias_kind":"pith_short_8","alias_value":"XAM5IXLX","created_at":"2026-07-05T07:31:11Z"}],"graph_snapshots":[{"event_id":"sha256:ef466607fbdedf66277885510a19260782438de4f2114cd282bf3467a9473bc0","target":"graph","created_at":"2026-07-05T07:31:11Z","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/2401.03619/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Stochastic gradient descent (SGD) and its many variants are the widespread optimization algorithms for training deep neural networks. However, SGD suffers from inevitable drawbacks, including vanishing gradients, lack of theoretical guarantees, and substantial sensitivity to input. The Alternating Direction Method of Multipliers (ADMM) has been proposed to address these shortcomings as an effective alternative to the gradient-based methods. It has been successfully employed for training deep neural networks. However, ADMM-based optimizers have a slow convergence rate. This paper proposes an An","authors_text":"Gustavo Batista, Mohammad Deghat, Zeinab Ebrahimi","cross_cats":["cs.SY","eess.SY"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-01-08T01:22:00Z","title":"AA-DLADMM: An Accelerated ADMM-based Framework for Training Deep Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.03619","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:eea17cba07caa9fb1cebd7201497e1779f0fa7752efcf0fb9615fdb236d6ac35","target":"record","created_at":"2026-07-05T07:31:11Z","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":"781865ea04a0cd3f0ac5c9b50ee259b215bfc070dd56aa39ae458395cbcabe02","cross_cats_sorted":["cs.SY","eess.SY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-01-08T01:22:00Z","title_canon_sha256":"06c6dcdac96b6f95246bbb32f30141e10d1ef9812341029508ae4dea1672e687"},"schema_version":"1.0","source":{"id":"2401.03619","kind":"arxiv","version":1}},"canonical_sha256":"b819d45d770c46ff476cece6fdc7aef9556ec0da03748a0707fabec848284946","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b819d45d770c46ff476cece6fdc7aef9556ec0da03748a0707fabec848284946","first_computed_at":"2026-07-05T07:31:11.664906Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:31:11.664906Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+YoIaZTwXRjRrsxc9xU9ctwOzbUb15ArrTUxF9Q3JDIIrmBZRRyPHvuAERL3tOlrE6fcmp+YawmRIWy82MjHBg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:31:11.665404Z","signed_message":"canonical_sha256_bytes"},"source_id":"2401.03619","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eea17cba07caa9fb1cebd7201497e1779f0fa7752efcf0fb9615fdb236d6ac35","sha256:ef466607fbdedf66277885510a19260782438de4f2114cd282bf3467a9473bc0"],"state_sha256":"9b59dcbcd377f3b3a86db5dd8e7eb4681c9ea386e6b958e226e27947596e398a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gyK8TIDAkQ3KShjCejgVrtTenqeBmj9d1TbW+ly0HiUuVFxEMhrN+Lk5iyzWl910eyjE8Csf5DqDYx9VUim/Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T17:13:17.780279Z","bundle_sha256":"13d424caf75e79f650bb289b026e3703c0150e76f93bf5c3314a2162539a44d4"}}