{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:APVYUXDLP3QLPJAHG4HTDI66B2","short_pith_number":"pith:APVYUXDL","canonical_record":{"source":{"id":"2202.00980","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-02-02T11:58:56Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"a25495f0841873cfc7387b07740f85e541aeefc80379b1e0aea33ada0e0d9200","abstract_canon_sha256":"0835c364bfef58e7120f1fa96ea91c248efd31ada2b0997848b9b04743d89dbc"},"schema_version":"1.0"},"canonical_sha256":"03eb8a5c6b7ee0b7a407370f31a3de0e979b6ee29efb1a9dbcae203ffa32a800","source":{"kind":"arxiv","id":"2202.00980","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.00980","created_at":"2026-07-05T04:41:11Z"},{"alias_kind":"arxiv_version","alias_value":"2202.00980v2","created_at":"2026-07-05T04:41:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.00980","created_at":"2026-07-05T04:41:11Z"},{"alias_kind":"pith_short_12","alias_value":"APVYUXDLP3QL","created_at":"2026-07-05T04:41:11Z"},{"alias_kind":"pith_short_16","alias_value":"APVYUXDLP3QLPJAH","created_at":"2026-07-05T04:41:11Z"},{"alias_kind":"pith_short_8","alias_value":"APVYUXDL","created_at":"2026-07-05T04:41:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:APVYUXDLP3QLPJAHG4HTDI66B2","target":"record","payload":{"canonical_record":{"source":{"id":"2202.00980","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-02-02T11:58:56Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"a25495f0841873cfc7387b07740f85e541aeefc80379b1e0aea33ada0e0d9200","abstract_canon_sha256":"0835c364bfef58e7120f1fa96ea91c248efd31ada2b0997848b9b04743d89dbc"},"schema_version":"1.0"},"canonical_sha256":"03eb8a5c6b7ee0b7a407370f31a3de0e979b6ee29efb1a9dbcae203ffa32a800","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:41:11.228270Z","signature_b64":"XOQsUHi4z/tYUOOCrCGpY1VOUUSgsVS4HbuM9UT7LIUWq2oUbngAa4+KwXZ1Ph6ZAuY3nfYM4+I8Rtf/kM/GCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"03eb8a5c6b7ee0b7a407370f31a3de0e979b6ee29efb1a9dbcae203ffa32a800","last_reissued_at":"2026-07-05T04:41:11.227915Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:41:11.227915Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2202.00980","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":"2026-07-05T04:41:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wiHOW0YldTg3egryZCtR3CIqXgv6RvPtdDKu9auQyHyyJKEIYNaMICGHTD61OpKKD7scN7snNmrC/flIyJ53AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T16:45:30.168494Z"},"content_sha256":"8c0cf04a5fb6962d098df37f55846498a631b5702d5bd48e06eaed5797bc68e5","schema_version":"1.0","event_id":"sha256:8c0cf04a5fb6962d098df37f55846498a631b5702d5bd48e06eaed5797bc68e5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:APVYUXDLP3QLPJAHG4HTDI66B2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Robust Training of Neural Networks Using Scale Invariant Architectures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Manzil Zaheer, Sanjiv Kumar, Sashank J. Reddi, Srinadh Bhojanapalli, Zhiyuan Li","submitted_at":"2022-02-02T11:58:56Z","abstract_excerpt":"In contrast to SGD, adaptive gradient methods like Adam allow robust training of modern deep networks, especially large language models. However, the use of adaptivity not only comes at the cost of extra memory but also raises the fundamental question: can non-adaptive methods like SGD enjoy similar benefits? In this paper, we provide an affirmative answer to this question by proposing to achieve both robust and memory-efficient training via the following general recipe: (1) modify the architecture and make it scale invariant, i.e. the scale of parameter doesn't affect the output of the networ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.00980","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/2202.00980/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":"2026-07-05T04:41:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"w2ZJfwzCsbcIa7fkzhy7sV0WAuwylJkIXxqJ/rA9sxYP7izm2ENWtzu2vVU4ZzhPWMy3V8qXIWTCebtdaE8dBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T16:45:30.169447Z"},"content_sha256":"c5a3ef3d41c43b7acaaf9a3cfae95bb5f7144cf3d7bd609c1f3907bc53b023b5","schema_version":"1.0","event_id":"sha256:c5a3ef3d41c43b7acaaf9a3cfae95bb5f7144cf3d7bd609c1f3907bc53b023b5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/APVYUXDLP3QLPJAHG4HTDI66B2/bundle.json","state_url":"https://pith.science/pith/APVYUXDLP3QLPJAHG4HTDI66B2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/APVYUXDLP3QLPJAHG4HTDI66B2/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":"2026-08-04T16:45:30Z","links":{"resolver":"https://pith.science/pith/APVYUXDLP3QLPJAHG4HTDI66B2","bundle":"https://pith.science/pith/APVYUXDLP3QLPJAHG4HTDI66B2/bundle.json","state":"https://pith.science/pith/APVYUXDLP3QLPJAHG4HTDI66B2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/APVYUXDLP3QLPJAHG4HTDI66B2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:APVYUXDLP3QLPJAHG4HTDI66B2","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":"0835c364bfef58e7120f1fa96ea91c248efd31ada2b0997848b9b04743d89dbc","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-02-02T11:58:56Z","title_canon_sha256":"a25495f0841873cfc7387b07740f85e541aeefc80379b1e0aea33ada0e0d9200"},"schema_version":"1.0","source":{"id":"2202.00980","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.00980","created_at":"2026-07-05T04:41:11Z"},{"alias_kind":"arxiv_version","alias_value":"2202.00980v2","created_at":"2026-07-05T04:41:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.00980","created_at":"2026-07-05T04:41:11Z"},{"alias_kind":"pith_short_12","alias_value":"APVYUXDLP3QL","created_at":"2026-07-05T04:41:11Z"},{"alias_kind":"pith_short_16","alias_value":"APVYUXDLP3QLPJAH","created_at":"2026-07-05T04:41:11Z"},{"alias_kind":"pith_short_8","alias_value":"APVYUXDL","created_at":"2026-07-05T04:41:11Z"}],"graph_snapshots":[{"event_id":"sha256:c5a3ef3d41c43b7acaaf9a3cfae95bb5f7144cf3d7bd609c1f3907bc53b023b5","target":"graph","created_at":"2026-07-05T04:41:11Z","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/2202.00980/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In contrast to SGD, adaptive gradient methods like Adam allow robust training of modern deep networks, especially large language models. However, the use of adaptivity not only comes at the cost of extra memory but also raises the fundamental question: can non-adaptive methods like SGD enjoy similar benefits? In this paper, we provide an affirmative answer to this question by proposing to achieve both robust and memory-efficient training via the following general recipe: (1) modify the architecture and make it scale invariant, i.e. the scale of parameter doesn't affect the output of the networ","authors_text":"Manzil Zaheer, Sanjiv Kumar, Sashank J. Reddi, Srinadh Bhojanapalli, Zhiyuan Li","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-02-02T11:58:56Z","title":"Robust Training of Neural Networks Using Scale Invariant Architectures"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.00980","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":"sha256:8c0cf04a5fb6962d098df37f55846498a631b5702d5bd48e06eaed5797bc68e5","target":"record","created_at":"2026-07-05T04:41:11Z","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":"0835c364bfef58e7120f1fa96ea91c248efd31ada2b0997848b9b04743d89dbc","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-02-02T11:58:56Z","title_canon_sha256":"a25495f0841873cfc7387b07740f85e541aeefc80379b1e0aea33ada0e0d9200"},"schema_version":"1.0","source":{"id":"2202.00980","kind":"arxiv","version":2}},"canonical_sha256":"03eb8a5c6b7ee0b7a407370f31a3de0e979b6ee29efb1a9dbcae203ffa32a800","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"03eb8a5c6b7ee0b7a407370f31a3de0e979b6ee29efb1a9dbcae203ffa32a800","first_computed_at":"2026-07-05T04:41:11.227915Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:41:11.227915Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XOQsUHi4z/tYUOOCrCGpY1VOUUSgsVS4HbuM9UT7LIUWq2oUbngAa4+KwXZ1Ph6ZAuY3nfYM4+I8Rtf/kM/GCA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:41:11.228270Z","signed_message":"canonical_sha256_bytes"},"source_id":"2202.00980","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8c0cf04a5fb6962d098df37f55846498a631b5702d5bd48e06eaed5797bc68e5","sha256:c5a3ef3d41c43b7acaaf9a3cfae95bb5f7144cf3d7bd609c1f3907bc53b023b5"],"state_sha256":"3fc9241f96232332f9f1b0343f1f7864fedda66864a7a48ebc45ff42a6cac9f8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xWTfHeGHY4UViCoxxeryPcArd06MIO6g3+vg9gSBmTCM1F74u1wD4SEvV8GBujtN4wJgmxpXwmh1uz16kH6xDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T16:45:30.174393Z","bundle_sha256":"6a894dc43bcf25decd4054cbcaa8c39edb75061dd261fe5a89a91d42ef1b25c1"}}