{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:Q32SFR46NB3ZXS7HJ4DTR5ETAD","short_pith_number":"pith:Q32SFR46","canonical_record":{"source":{"id":"2311.00531","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2023-11-01T14:09:12Z","cross_cats_sorted":[],"title_canon_sha256":"7bf37de07021d959d3875cd908a628d7728128b4e6cb24dcf8cbb3607100d05a","abstract_canon_sha256":"cdc76a5d1f9bcc4bd297128bb3e3af6beb9e529d71e5ac52151f4c1f5d18ad81"},"schema_version":"1.0"},"canonical_sha256":"86f522c79e68779bcbe74f0738f49300ff997bef05e624e47e0768033bee4efa","source":{"kind":"arxiv","id":"2311.00531","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.00531","created_at":"2026-07-05T09:36:37Z"},{"alias_kind":"arxiv_version","alias_value":"2311.00531v3","created_at":"2026-07-05T09:36:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.00531","created_at":"2026-07-05T09:36:37Z"},{"alias_kind":"pith_short_12","alias_value":"Q32SFR46NB3Z","created_at":"2026-07-05T09:36:37Z"},{"alias_kind":"pith_short_16","alias_value":"Q32SFR46NB3ZXS7H","created_at":"2026-07-05T09:36:37Z"},{"alias_kind":"pith_short_8","alias_value":"Q32SFR46","created_at":"2026-07-05T09:36:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:Q32SFR46NB3ZXS7HJ4DTR5ETAD","target":"record","payload":{"canonical_record":{"source":{"id":"2311.00531","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2023-11-01T14:09:12Z","cross_cats_sorted":[],"title_canon_sha256":"7bf37de07021d959d3875cd908a628d7728128b4e6cb24dcf8cbb3607100d05a","abstract_canon_sha256":"cdc76a5d1f9bcc4bd297128bb3e3af6beb9e529d71e5ac52151f4c1f5d18ad81"},"schema_version":"1.0"},"canonical_sha256":"86f522c79e68779bcbe74f0738f49300ff997bef05e624e47e0768033bee4efa","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:36:37.215106Z","signature_b64":"2Ix35o02FJzDIshHna7UEjnwWOMb+6q6b44qPn3KukGmDjfiIAE15Nah6Qzz0HAOu2og/IOZ5Rm2QWPXCcE2Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"86f522c79e68779bcbe74f0738f49300ff997bef05e624e47e0768033bee4efa","last_reissued_at":"2026-07-05T09:36:37.214718Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:36:37.214718Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2311.00531","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:36:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AGR16KS9YXIgDXnPsbz8dXcOvhpfdXEWSkeC0rs3lcZ0KCmlr+/4PR/RQHiQAYiU2rBMDHU1x1GoMGaexDZaCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T01:38:19.207887Z"},"content_sha256":"fd7f58874a9d174483083fd781c05cb6ff17464cee3decd95dccdfb2f5d326ee","schema_version":"1.0","event_id":"sha256:fd7f58874a9d174483083fd781c05cb6ff17464cee3decd95dccdfb2f5d326ee"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:Q32SFR46NB3ZXS7HJ4DTR5ETAD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Improved Performance of Stochastic Gradients with Gaussian Smoothing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Andrew Starnes, Clayton Webster","submitted_at":"2023-11-01T14:09:12Z","abstract_excerpt":"This paper formalizes and analyzes Gaussian smoothing applied to two prominent optimization methods: Stochastic Gradient Descent (GSmoothSGD) and Adam (GSmoothAdam) in deep learning. By attenuating small fluctuations, Gaussian smoothing lowers the risk of gradient-based algorithms converging to poor local minima. These methods simplify the loss landscape while boosting robustness to noise and improving generalization, helping base algorithms converge more effectively to global minima. Existing approaches often rely on zero-order approximations, which increase training time due to inefficiencie"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.00531","kind":"arxiv","version":3},"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/2311.00531/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":"2026-07-05T09:36:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iaNKMvJKc31q5Uw9pDnTjBa8rWhWfZ80jmhv3Oq6/msoVP1T7cOuglrzaTbhy9KwfAQQ0S1kf0ewSw1AmL8VCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T01:38:19.208406Z"},"content_sha256":"35a17cc24ed958ad996a333746a97300d3c45dcf7b43fe9c5074db6d55fb3735","schema_version":"1.0","event_id":"sha256:35a17cc24ed958ad996a333746a97300d3c45dcf7b43fe9c5074db6d55fb3735"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Q32SFR46NB3ZXS7HJ4DTR5ETAD/bundle.json","state_url":"https://pith.science/pith/Q32SFR46NB3ZXS7HJ4DTR5ETAD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Q32SFR46NB3ZXS7HJ4DTR5ETAD/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":"2026-08-09T01:38:19Z","links":{"resolver":"https://pith.science/pith/Q32SFR46NB3ZXS7HJ4DTR5ETAD","bundle":"https://pith.science/pith/Q32SFR46NB3ZXS7HJ4DTR5ETAD/bundle.json","state":"https://pith.science/pith/Q32SFR46NB3ZXS7HJ4DTR5ETAD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Q32SFR46NB3ZXS7HJ4DTR5ETAD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:Q32SFR46NB3ZXS7HJ4DTR5ETAD","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":"cdc76a5d1f9bcc4bd297128bb3e3af6beb9e529d71e5ac52151f4c1f5d18ad81","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2023-11-01T14:09:12Z","title_canon_sha256":"7bf37de07021d959d3875cd908a628d7728128b4e6cb24dcf8cbb3607100d05a"},"schema_version":"1.0","source":{"id":"2311.00531","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.00531","created_at":"2026-07-05T09:36:37Z"},{"alias_kind":"arxiv_version","alias_value":"2311.00531v3","created_at":"2026-07-05T09:36:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.00531","created_at":"2026-07-05T09:36:37Z"},{"alias_kind":"pith_short_12","alias_value":"Q32SFR46NB3Z","created_at":"2026-07-05T09:36:37Z"},{"alias_kind":"pith_short_16","alias_value":"Q32SFR46NB3ZXS7H","created_at":"2026-07-05T09:36:37Z"},{"alias_kind":"pith_short_8","alias_value":"Q32SFR46","created_at":"2026-07-05T09:36:37Z"}],"graph_snapshots":[{"event_id":"sha256:35a17cc24ed958ad996a333746a97300d3c45dcf7b43fe9c5074db6d55fb3735","target":"graph","created_at":"2026-07-05T09:36:37Z","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/2311.00531/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper formalizes and analyzes Gaussian smoothing applied to two prominent optimization methods: Stochastic Gradient Descent (GSmoothSGD) and Adam (GSmoothAdam) in deep learning. By attenuating small fluctuations, Gaussian smoothing lowers the risk of gradient-based algorithms converging to poor local minima. These methods simplify the loss landscape while boosting robustness to noise and improving generalization, helping base algorithms converge more effectively to global minima. Existing approaches often rely on zero-order approximations, which increase training time due to inefficiencie","authors_text":"Andrew Starnes, Clayton Webster","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2023-11-01T14:09:12Z","title":"Improved Performance of Stochastic Gradients with Gaussian Smoothing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.00531","kind":"arxiv","version":3},"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:fd7f58874a9d174483083fd781c05cb6ff17464cee3decd95dccdfb2f5d326ee","target":"record","created_at":"2026-07-05T09:36:37Z","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":"cdc76a5d1f9bcc4bd297128bb3e3af6beb9e529d71e5ac52151f4c1f5d18ad81","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2023-11-01T14:09:12Z","title_canon_sha256":"7bf37de07021d959d3875cd908a628d7728128b4e6cb24dcf8cbb3607100d05a"},"schema_version":"1.0","source":{"id":"2311.00531","kind":"arxiv","version":3}},"canonical_sha256":"86f522c79e68779bcbe74f0738f49300ff997bef05e624e47e0768033bee4efa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"86f522c79e68779bcbe74f0738f49300ff997bef05e624e47e0768033bee4efa","first_computed_at":"2026-07-05T09:36:37.214718Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:36:37.214718Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2Ix35o02FJzDIshHna7UEjnwWOMb+6q6b44qPn3KukGmDjfiIAE15Nah6Qzz0HAOu2og/IOZ5Rm2QWPXCcE2Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:36:37.215106Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.00531","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fd7f58874a9d174483083fd781c05cb6ff17464cee3decd95dccdfb2f5d326ee","sha256:35a17cc24ed958ad996a333746a97300d3c45dcf7b43fe9c5074db6d55fb3735"],"state_sha256":"08e30e9c0c1d9aa8c5e11fcca1cc62b2fa95fab0129fdece9c79d6ddbe3a2f48"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MiNGPjQHZ9O6EWZPxYw4I3U6sC1Ab+y0PwOX/n3ETMR2ymfm8tHU9J1tCKX3uycL8igCboLrGXQ2nmbXzMQADQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T01:38:19.213443Z","bundle_sha256":"332a5194cdd3dbbf756382d9425029bd41b937cd7491c8896779af61e1d9cd09"}}