{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:OKKUGJRG34UZFUSHENKF5C3CEX","short_pith_number":"pith:OKKUGJRG","canonical_record":{"source":{"id":"2112.00029","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-11-30T19:00:03Z","cross_cats_sorted":[],"title_canon_sha256":"a4d455ba15156a31a20e7e5caf34e6abcea36b3bef9792706ceab2348b4973b4","abstract_canon_sha256":"84f731bfe60bce6093d993da60e7ca6f37a9b8a1e954acc40042bdaf2e486945"},"schema_version":"1.0"},"canonical_sha256":"7295432626df2992d24723545e8b6225ecc9b35319576ee71a284d5d99087b52","source":{"kind":"arxiv","id":"2112.00029","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2112.00029","created_at":"2026-07-05T04:22:18Z"},{"alias_kind":"arxiv_version","alias_value":"2112.00029v2","created_at":"2026-07-05T04:22:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.00029","created_at":"2026-07-05T04:22:18Z"},{"alias_kind":"pith_short_12","alias_value":"OKKUGJRG34UZ","created_at":"2026-07-05T04:22:18Z"},{"alias_kind":"pith_short_16","alias_value":"OKKUGJRG34UZFUSH","created_at":"2026-07-05T04:22:18Z"},{"alias_kind":"pith_short_8","alias_value":"OKKUGJRG","created_at":"2026-07-05T04:22:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:OKKUGJRG34UZFUSHENKF5C3CEX","target":"record","payload":{"canonical_record":{"source":{"id":"2112.00029","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-11-30T19:00:03Z","cross_cats_sorted":[],"title_canon_sha256":"a4d455ba15156a31a20e7e5caf34e6abcea36b3bef9792706ceab2348b4973b4","abstract_canon_sha256":"84f731bfe60bce6093d993da60e7ca6f37a9b8a1e954acc40042bdaf2e486945"},"schema_version":"1.0"},"canonical_sha256":"7295432626df2992d24723545e8b6225ecc9b35319576ee71a284d5d99087b52","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:22:18.386186Z","signature_b64":"EUrS5NBnKhfkFP7SRDr+pL8jpmoSbrEb2WIWdkXGxARMPH0IfNis0IzLhXWajKGCC92N0VQ1vyGukM9iPVOgDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7295432626df2992d24723545e8b6225ecc9b35319576ee71a284d5d99087b52","last_reissued_at":"2026-07-05T04:22:18.385617Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:22:18.385617Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2112.00029","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":"2026-07-05T04:22:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"T2ZsOot7FInqoZazOhGOlROcjfpc+tzU7SROkURN0aSXzboembreQciohUUWE6IHfEiVbjQEfMRr//LyknfGAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T23:10:16.557949Z"},"content_sha256":"439f64cdcdf369861eab079ac4d31c854e5f48a1b5b0da180189c973c6de9256","schema_version":"1.0","event_id":"sha256:439f64cdcdf369861eab079ac4d31c854e5f48a1b5b0da180189c973c6de9256"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:OKKUGJRG34UZFUSHENKF5C3CEX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Atri Rudra, Beidi Chen, Christopher R\\'e, Jiaming Yang, Kaizhao Liang, Tri Dao, Zhao Song","submitted_at":"2021-11-30T19:00:03Z","abstract_excerpt":"Overparameterized neural networks generalize well but are expensive to train. Ideally, one would like to reduce their computational cost while retaining their generalization benefits. Sparse model training is a simple and promising approach to achieve this, but there remain challenges as existing methods struggle with accuracy loss, slow training runtime, or difficulty in sparsifying all model components. The core problem is that searching for a sparsity mask over a discrete set of sparse matrices is difficult and expensive. To address this, our main insight is to optimize over a continuous su"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.00029","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/2112.00029/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":"2026-07-05T04:22:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+DCb9QBxORusbAlJZA4V1UJF+FiPR1FxfAbWKqlavl/khksDWOkaimsuKyT5ee4uLjAadRJnujP2NrlXZA4sAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T23:10:16.558335Z"},"content_sha256":"3140cee880bb05191b46e427c7b92089e20251d2bd37dcffb0ffbe0c7b9f6c42","schema_version":"1.0","event_id":"sha256:3140cee880bb05191b46e427c7b92089e20251d2bd37dcffb0ffbe0c7b9f6c42"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OKKUGJRG34UZFUSHENKF5C3CEX/bundle.json","state_url":"https://pith.science/pith/OKKUGJRG34UZFUSHENKF5C3CEX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OKKUGJRG34UZFUSHENKF5C3CEX/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":"2026-07-06T23:10:16Z","links":{"resolver":"https://pith.science/pith/OKKUGJRG34UZFUSHENKF5C3CEX","bundle":"https://pith.science/pith/OKKUGJRG34UZFUSHENKF5C3CEX/bundle.json","state":"https://pith.science/pith/OKKUGJRG34UZFUSHENKF5C3CEX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OKKUGJRG34UZFUSHENKF5C3CEX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:OKKUGJRG34UZFUSHENKF5C3CEX","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":"84f731bfe60bce6093d993da60e7ca6f37a9b8a1e954acc40042bdaf2e486945","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-11-30T19:00:03Z","title_canon_sha256":"a4d455ba15156a31a20e7e5caf34e6abcea36b3bef9792706ceab2348b4973b4"},"schema_version":"1.0","source":{"id":"2112.00029","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2112.00029","created_at":"2026-07-05T04:22:18Z"},{"alias_kind":"arxiv_version","alias_value":"2112.00029v2","created_at":"2026-07-05T04:22:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.00029","created_at":"2026-07-05T04:22:18Z"},{"alias_kind":"pith_short_12","alias_value":"OKKUGJRG34UZ","created_at":"2026-07-05T04:22:18Z"},{"alias_kind":"pith_short_16","alias_value":"OKKUGJRG34UZFUSH","created_at":"2026-07-05T04:22:18Z"},{"alias_kind":"pith_short_8","alias_value":"OKKUGJRG","created_at":"2026-07-05T04:22:18Z"}],"graph_snapshots":[{"event_id":"sha256:3140cee880bb05191b46e427c7b92089e20251d2bd37dcffb0ffbe0c7b9f6c42","target":"graph","created_at":"2026-07-05T04:22:18Z","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/2112.00029/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Overparameterized neural networks generalize well but are expensive to train. Ideally, one would like to reduce their computational cost while retaining their generalization benefits. Sparse model training is a simple and promising approach to achieve this, but there remain challenges as existing methods struggle with accuracy loss, slow training runtime, or difficulty in sparsifying all model components. The core problem is that searching for a sparsity mask over a discrete set of sparse matrices is difficult and expensive. To address this, our main insight is to optimize over a continuous su","authors_text":"Atri Rudra, Beidi Chen, Christopher R\\'e, Jiaming Yang, Kaizhao Liang, Tri Dao, Zhao Song","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-11-30T19:00:03Z","title":"Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.00029","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":"sha256:439f64cdcdf369861eab079ac4d31c854e5f48a1b5b0da180189c973c6de9256","target":"record","created_at":"2026-07-05T04:22:18Z","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":"84f731bfe60bce6093d993da60e7ca6f37a9b8a1e954acc40042bdaf2e486945","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-11-30T19:00:03Z","title_canon_sha256":"a4d455ba15156a31a20e7e5caf34e6abcea36b3bef9792706ceab2348b4973b4"},"schema_version":"1.0","source":{"id":"2112.00029","kind":"arxiv","version":2}},"canonical_sha256":"7295432626df2992d24723545e8b6225ecc9b35319576ee71a284d5d99087b52","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7295432626df2992d24723545e8b6225ecc9b35319576ee71a284d5d99087b52","first_computed_at":"2026-07-05T04:22:18.385617Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:22:18.385617Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EUrS5NBnKhfkFP7SRDr+pL8jpmoSbrEb2WIWdkXGxARMPH0IfNis0IzLhXWajKGCC92N0VQ1vyGukM9iPVOgDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:22:18.386186Z","signed_message":"canonical_sha256_bytes"},"source_id":"2112.00029","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:439f64cdcdf369861eab079ac4d31c854e5f48a1b5b0da180189c973c6de9256","sha256:3140cee880bb05191b46e427c7b92089e20251d2bd37dcffb0ffbe0c7b9f6c42"],"state_sha256":"4dfd96304e8c103be18e885a9b068d7e5cbdec7f39cbdb8da5c7f805614a1055"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"a7/5pFot3XWFG3mxs0xzlhRaxkSlb8SnElH5zf6ZmklDiY1ATUgYUXbO/V35rTuU0i6cdaVQ4z/+Uytra77hAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T23:10:16.560417Z","bundle_sha256":"17bca150daf9a8bbd9e18abdc210ac250939dd963864594a725bedd196b7acbb"}}