{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:PFOMXDIWNTIBC5QUCWCDIZY7Z2","short_pith_number":"pith:PFOMXDIW","canonical_record":{"source":{"id":"2012.02792","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-12-05T15:12:10Z","cross_cats_sorted":[],"title_canon_sha256":"b44c87844c379e14827392dde12096d8b4dc068637bca7bc75585e343a84b864","abstract_canon_sha256":"1801d545633a1995c69e79e151a7a819071ede59f3de11b10b8feb28091c3c63"},"schema_version":"1.0"},"canonical_sha256":"795ccb8d166cd0117614158434671fceb57ab7ba813fbf6b61442bd3f2cca052","source":{"kind":"arxiv","id":"2012.02792","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2012.02792","created_at":"2026-07-05T01:57:22Z"},{"alias_kind":"arxiv_version","alias_value":"2012.02792v1","created_at":"2026-07-05T01:57:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.02792","created_at":"2026-07-05T01:57:22Z"},{"alias_kind":"pith_short_12","alias_value":"PFOMXDIWNTIB","created_at":"2026-07-05T01:57:22Z"},{"alias_kind":"pith_short_16","alias_value":"PFOMXDIWNTIBC5QU","created_at":"2026-07-05T01:57:22Z"},{"alias_kind":"pith_short_8","alias_value":"PFOMXDIW","created_at":"2026-07-05T01:57:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:PFOMXDIWNTIBC5QUCWCDIZY7Z2","target":"record","payload":{"canonical_record":{"source":{"id":"2012.02792","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-12-05T15:12:10Z","cross_cats_sorted":[],"title_canon_sha256":"b44c87844c379e14827392dde12096d8b4dc068637bca7bc75585e343a84b864","abstract_canon_sha256":"1801d545633a1995c69e79e151a7a819071ede59f3de11b10b8feb28091c3c63"},"schema_version":"1.0"},"canonical_sha256":"795ccb8d166cd0117614158434671fceb57ab7ba813fbf6b61442bd3f2cca052","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:57:22.610220Z","signature_b64":"9jTQucwRNmlpzjZdUvYauqdtS/mouGaUuvN+C1RBe5ppZ1CIBckdTJnafeJzqimk0QmS/s6zTNIGpdTGNRIDAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"795ccb8d166cd0117614158434671fceb57ab7ba813fbf6b61442bd3f2cca052","last_reissued_at":"2026-07-05T01:57:22.609727Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:57:22.609727Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2012.02792","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":"2026-07-05T01:57:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"epbKPpFqjQvB7gk0pXZGVL9XVXrhrIxIxYESKb0ZPGevBCB7p6w37bkaJYfncKtOBjhvbZWq+zwdOOyjAfquDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T15:02:23.844365Z"},"content_sha256":"e34fbe4083c79eaef756ea5820ef830a0e7b9e5cc4047b6fb0afba5db6074c0b","schema_version":"1.0","event_id":"sha256:e34fbe4083c79eaef756ea5820ef830a0e7b9e5cc4047b6fb0afba5db6074c0b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:PFOMXDIWNTIBC5QUCWCDIZY7Z2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Weight Update Skipping: Reducing Training Time for Artificial Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ismail Akturk, Pooneh Safayenikoo","submitted_at":"2020-12-05T15:12:10Z","abstract_excerpt":"Artificial Neural Networks (ANNs) are known as state-of-the-art techniques in Machine Learning (ML) and have achieved outstanding results in data-intensive applications, such as recognition, classification, and segmentation. These networks mostly use deep layers of convolution or fully connected layers with many filters in each layer, demanding a large amount of data and tunable hyperparameters to achieve competitive accuracy. As a result, storage, communication, and computational costs of training (in particular training time) become limiting factors to scale them up. In this paper, we propos"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.02792","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/2012.02792/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":"2026-07-05T01:57:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4O1Y1JEdeG9hQxBLPm9Xx0mYg5BJLPZrBHouTMLE1nUhuo+hfhl16hqK8+/WfV930kGzWSBthumbw7HCFLEZBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T15:02:23.845379Z"},"content_sha256":"56093c51c594efe921d9973182cd10b085588e832e136b20218eb8026ed66de7","schema_version":"1.0","event_id":"sha256:56093c51c594efe921d9973182cd10b085588e832e136b20218eb8026ed66de7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PFOMXDIWNTIBC5QUCWCDIZY7Z2/bundle.json","state_url":"https://pith.science/pith/PFOMXDIWNTIBC5QUCWCDIZY7Z2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PFOMXDIWNTIBC5QUCWCDIZY7Z2/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":"2026-08-10T15:02:23Z","links":{"resolver":"https://pith.science/pith/PFOMXDIWNTIBC5QUCWCDIZY7Z2","bundle":"https://pith.science/pith/PFOMXDIWNTIBC5QUCWCDIZY7Z2/bundle.json","state":"https://pith.science/pith/PFOMXDIWNTIBC5QUCWCDIZY7Z2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PFOMXDIWNTIBC5QUCWCDIZY7Z2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:PFOMXDIWNTIBC5QUCWCDIZY7Z2","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":"1801d545633a1995c69e79e151a7a819071ede59f3de11b10b8feb28091c3c63","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-12-05T15:12:10Z","title_canon_sha256":"b44c87844c379e14827392dde12096d8b4dc068637bca7bc75585e343a84b864"},"schema_version":"1.0","source":{"id":"2012.02792","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2012.02792","created_at":"2026-07-05T01:57:22Z"},{"alias_kind":"arxiv_version","alias_value":"2012.02792v1","created_at":"2026-07-05T01:57:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.02792","created_at":"2026-07-05T01:57:22Z"},{"alias_kind":"pith_short_12","alias_value":"PFOMXDIWNTIB","created_at":"2026-07-05T01:57:22Z"},{"alias_kind":"pith_short_16","alias_value":"PFOMXDIWNTIBC5QU","created_at":"2026-07-05T01:57:22Z"},{"alias_kind":"pith_short_8","alias_value":"PFOMXDIW","created_at":"2026-07-05T01:57:22Z"}],"graph_snapshots":[{"event_id":"sha256:56093c51c594efe921d9973182cd10b085588e832e136b20218eb8026ed66de7","target":"graph","created_at":"2026-07-05T01:57:22Z","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/2012.02792/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Artificial Neural Networks (ANNs) are known as state-of-the-art techniques in Machine Learning (ML) and have achieved outstanding results in data-intensive applications, such as recognition, classification, and segmentation. These networks mostly use deep layers of convolution or fully connected layers with many filters in each layer, demanding a large amount of data and tunable hyperparameters to achieve competitive accuracy. As a result, storage, communication, and computational costs of training (in particular training time) become limiting factors to scale them up. In this paper, we propos","authors_text":"Ismail Akturk, Pooneh Safayenikoo","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-12-05T15:12:10Z","title":"Weight Update Skipping: Reducing Training Time for Artificial Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.02792","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:e34fbe4083c79eaef756ea5820ef830a0e7b9e5cc4047b6fb0afba5db6074c0b","target":"record","created_at":"2026-07-05T01:57:22Z","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":"1801d545633a1995c69e79e151a7a819071ede59f3de11b10b8feb28091c3c63","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-12-05T15:12:10Z","title_canon_sha256":"b44c87844c379e14827392dde12096d8b4dc068637bca7bc75585e343a84b864"},"schema_version":"1.0","source":{"id":"2012.02792","kind":"arxiv","version":1}},"canonical_sha256":"795ccb8d166cd0117614158434671fceb57ab7ba813fbf6b61442bd3f2cca052","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"795ccb8d166cd0117614158434671fceb57ab7ba813fbf6b61442bd3f2cca052","first_computed_at":"2026-07-05T01:57:22.609727Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:57:22.609727Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9jTQucwRNmlpzjZdUvYauqdtS/mouGaUuvN+C1RBe5ppZ1CIBckdTJnafeJzqimk0QmS/s6zTNIGpdTGNRIDAg==","signature_status":"signed_v1","signed_at":"2026-07-05T01:57:22.610220Z","signed_message":"canonical_sha256_bytes"},"source_id":"2012.02792","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e34fbe4083c79eaef756ea5820ef830a0e7b9e5cc4047b6fb0afba5db6074c0b","sha256:56093c51c594efe921d9973182cd10b085588e832e136b20218eb8026ed66de7"],"state_sha256":"2e689b9a712bd49f542f4e588b88d57eb378ea17f613070e28a1af7cd05708e9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Z4Ca3hiUZ5+A+I0sf49DsadAGcfIzayr/rSAtkzzKEacWVxfmfsN7YdJJ7jRUV8HChYoffP1/y+Yqpx03vJvDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T15:02:23.851605Z","bundle_sha256":"046add4418374d9452799760c4670b80eea83d295153d69c6c477ab5bd2c33a2"}}