{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:44CG5JVO5QFSQOLGZPLFJL7ZNV","short_pith_number":"pith:44CG5JVO","canonical_record":{"source":{"id":"2305.07583","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-05-12T16:25:57Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"741bd7f615d635f7ff56a10501eb8b5f17af427a20b98ff55a8b319021b64366","abstract_canon_sha256":"6f505356e4cf80fec5a2704b7d5829918eeb232308abc35829a90ebadf560602"},"schema_version":"1.0"},"canonical_sha256":"e7046ea6aeec0b283966cbd654aff96d564e7f21e7e774ef1b08b69c0c2e5c90","source":{"kind":"arxiv","id":"2305.07583","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.07583","created_at":"2026-07-05T08:27:40Z"},{"alias_kind":"arxiv_version","alias_value":"2305.07583v3","created_at":"2026-07-05T08:27:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.07583","created_at":"2026-07-05T08:27:40Z"},{"alias_kind":"pith_short_12","alias_value":"44CG5JVO5QFS","created_at":"2026-07-05T08:27:40Z"},{"alias_kind":"pith_short_16","alias_value":"44CG5JVO5QFSQOLG","created_at":"2026-07-05T08:27:40Z"},{"alias_kind":"pith_short_8","alias_value":"44CG5JVO","created_at":"2026-07-05T08:27:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:44CG5JVO5QFSQOLGZPLFJL7ZNV","target":"record","payload":{"canonical_record":{"source":{"id":"2305.07583","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-05-12T16:25:57Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"741bd7f615d635f7ff56a10501eb8b5f17af427a20b98ff55a8b319021b64366","abstract_canon_sha256":"6f505356e4cf80fec5a2704b7d5829918eeb232308abc35829a90ebadf560602"},"schema_version":"1.0"},"canonical_sha256":"e7046ea6aeec0b283966cbd654aff96d564e7f21e7e774ef1b08b69c0c2e5c90","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:27:40.387159Z","signature_b64":"S2AXSQC8h70Q+5uyN+JZ2/ajDkWyLgKBRxSDtNY9189eUu4PM/4QgilOU0WdQG7rzJSfwwk6vCMVFFqKqvRIDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e7046ea6aeec0b283966cbd654aff96d564e7f21e7e774ef1b08b69c0c2e5c90","last_reissued_at":"2026-07-05T08:27:40.386685Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:27:40.386685Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.07583","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":"2026-07-05T08:27:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8JIYLx/6OSU0JQTcg3o1hiiXfoAq5CbMFNiQ2tf7HsSoXjk9qnlN2M6EaG7KXv13o+Ir6RDcM0L2AMKDqo3IDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T05:21:52.443869Z"},"content_sha256":"c1686e0be1a9ac7a57282035f1262ae1d9b32d1dba9fa708fe5fceda1b6ad950","schema_version":"1.0","event_id":"sha256:c1686e0be1a9ac7a57282035f1262ae1d9b32d1dba9fa708fe5fceda1b6ad950"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:44CG5JVO5QFSQOLGZPLFJL7ZNV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MoMo: Momentum Models for Adaptive Learning Rates","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.LG","authors_text":"Aaron Defazio, Fabian Schaipp, Michael Eickenberg, Robert M. Gower, Ruben Ohana","submitted_at":"2023-05-12T16:25:57Z","abstract_excerpt":"Training a modern machine learning architecture on a new task requires extensive learning-rate tuning, which comes at a high computational cost. Here we develop new Polyak-type adaptive learning rates that can be used on top of any momentum method, and require less tuning to perform well. We first develop MoMo, a Momentum Model based adaptive learning rate for SGD-M (stochastic gradient descent with momentum). MoMo uses momentum estimates of the losses and gradients sampled at each iteration to build a model of the loss function. Our model makes use of any known lower bound of the loss functio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.07583","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/2305.07583/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":"2026-07-05T08:27:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QHani+yYY3Ji0dOHpYHrFDv8bdPdvfeXzgKpjD8J7j4vdacMawzLu2uTqCujzHDZZqh0GOZUZrj+BPLz2km2DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T05:21:52.444379Z"},"content_sha256":"ba89fa9209c2803cd0259291ba85596f8ff91c9ce331562e00fb49c746c8d707","schema_version":"1.0","event_id":"sha256:ba89fa9209c2803cd0259291ba85596f8ff91c9ce331562e00fb49c746c8d707"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/44CG5JVO5QFSQOLGZPLFJL7ZNV/bundle.json","state_url":"https://pith.science/pith/44CG5JVO5QFSQOLGZPLFJL7ZNV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/44CG5JVO5QFSQOLGZPLFJL7ZNV/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":"2026-08-23T05:21:52Z","links":{"resolver":"https://pith.science/pith/44CG5JVO5QFSQOLGZPLFJL7ZNV","bundle":"https://pith.science/pith/44CG5JVO5QFSQOLGZPLFJL7ZNV/bundle.json","state":"https://pith.science/pith/44CG5JVO5QFSQOLGZPLFJL7ZNV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/44CG5JVO5QFSQOLGZPLFJL7ZNV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:44CG5JVO5QFSQOLGZPLFJL7ZNV","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":"6f505356e4cf80fec5a2704b7d5829918eeb232308abc35829a90ebadf560602","cross_cats_sorted":["math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-05-12T16:25:57Z","title_canon_sha256":"741bd7f615d635f7ff56a10501eb8b5f17af427a20b98ff55a8b319021b64366"},"schema_version":"1.0","source":{"id":"2305.07583","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.07583","created_at":"2026-07-05T08:27:40Z"},{"alias_kind":"arxiv_version","alias_value":"2305.07583v3","created_at":"2026-07-05T08:27:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.07583","created_at":"2026-07-05T08:27:40Z"},{"alias_kind":"pith_short_12","alias_value":"44CG5JVO5QFS","created_at":"2026-07-05T08:27:40Z"},{"alias_kind":"pith_short_16","alias_value":"44CG5JVO5QFSQOLG","created_at":"2026-07-05T08:27:40Z"},{"alias_kind":"pith_short_8","alias_value":"44CG5JVO","created_at":"2026-07-05T08:27:40Z"}],"graph_snapshots":[{"event_id":"sha256:ba89fa9209c2803cd0259291ba85596f8ff91c9ce331562e00fb49c746c8d707","target":"graph","created_at":"2026-07-05T08:27:40Z","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/2305.07583/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Training a modern machine learning architecture on a new task requires extensive learning-rate tuning, which comes at a high computational cost. Here we develop new Polyak-type adaptive learning rates that can be used on top of any momentum method, and require less tuning to perform well. We first develop MoMo, a Momentum Model based adaptive learning rate for SGD-M (stochastic gradient descent with momentum). MoMo uses momentum estimates of the losses and gradients sampled at each iteration to build a model of the loss function. Our model makes use of any known lower bound of the loss functio","authors_text":"Aaron Defazio, Fabian Schaipp, Michael Eickenberg, Robert M. Gower, Ruben Ohana","cross_cats":["math.OC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-05-12T16:25:57Z","title":"MoMo: Momentum Models for Adaptive Learning Rates"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.07583","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:c1686e0be1a9ac7a57282035f1262ae1d9b32d1dba9fa708fe5fceda1b6ad950","target":"record","created_at":"2026-07-05T08:27:40Z","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":"6f505356e4cf80fec5a2704b7d5829918eeb232308abc35829a90ebadf560602","cross_cats_sorted":["math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-05-12T16:25:57Z","title_canon_sha256":"741bd7f615d635f7ff56a10501eb8b5f17af427a20b98ff55a8b319021b64366"},"schema_version":"1.0","source":{"id":"2305.07583","kind":"arxiv","version":3}},"canonical_sha256":"e7046ea6aeec0b283966cbd654aff96d564e7f21e7e774ef1b08b69c0c2e5c90","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e7046ea6aeec0b283966cbd654aff96d564e7f21e7e774ef1b08b69c0c2e5c90","first_computed_at":"2026-07-05T08:27:40.386685Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:27:40.386685Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"S2AXSQC8h70Q+5uyN+JZ2/ajDkWyLgKBRxSDtNY9189eUu4PM/4QgilOU0WdQG7rzJSfwwk6vCMVFFqKqvRIDg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:27:40.387159Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.07583","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c1686e0be1a9ac7a57282035f1262ae1d9b32d1dba9fa708fe5fceda1b6ad950","sha256:ba89fa9209c2803cd0259291ba85596f8ff91c9ce331562e00fb49c746c8d707"],"state_sha256":"dfe708ee2c75b25a0b9ce4c4d984113a0387190de2d019e88032d102a92e18d0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OKjgXeE46agjYFZRWIpKiJF96VnJ5J5ZaQ3sgrVJ9ZPGXMeXJlPALH4ymcPMzvqjqcd+MEsgrd0rHkhrsTHxAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T05:21:52.449411Z","bundle_sha256":"11d79e4b5e96458e86916e67afea56baebf8491b6384a78f910d84c7772f6849"}}