{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:D2DSRQ5B72432AK7DI2UCDZOIA","short_pith_number":"pith:D2DSRQ5B","schema_version":"1.0","canonical_sha256":"1e8728c3a1feb9bd015f1a35410f2e4025b4a4edf88a8c9b052ae43b85d766a6","source":{"kind":"arxiv","id":"2506.08353","version":1},"attestation_state":"computed","paper":{"title":"An Adaptive Method Stabilizing Activations for Enhanced Generalization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Hyunseok Seung, Hyunsuk Ko, Jaewoo Lee","submitted_at":"2025-06-10T02:10:40Z","abstract_excerpt":"We introduce AdaAct, a novel optimization algorithm that adjusts learning rates according to activation variance. Our method enhances the stability of neuron outputs by incorporating neuron-wise adaptivity during the training process, which subsequently leads to better generalization -- a complementary approach to conventional activation regularization methods. Experimental results demonstrate AdaAct's competitive performance across standard image classification benchmarks. We evaluate AdaAct on CIFAR and ImageNet, comparing it with other state-of-the-art methods. Importantly, AdaAct effective"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2506.08353","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-10T02:10:40Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"9f78e7ad7286360cab721bd7de10177e8a32267560dc55bef58e433823f81d14","abstract_canon_sha256":"55b354ed4b400acbeb5c69f7137b08555caea519634c1456400ca0ea8abca754"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:18:48.442310Z","signature_b64":"siSGdvS6kKFOThxoGspDtcRQpG4rMKEdqBIqtDY9mLmNo6E6Fx7P7o7Xbrm/b/QBedmP9uVIG8n2OV/WM6CYAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1e8728c3a1feb9bd015f1a35410f2e4025b4a4edf88a8c9b052ae43b85d766a6","last_reissued_at":"2026-07-05T11:18:48.441972Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:18:48.441972Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Adaptive Method Stabilizing Activations for Enhanced Generalization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Hyunseok Seung, Hyunsuk Ko, Jaewoo Lee","submitted_at":"2025-06-10T02:10:40Z","abstract_excerpt":"We introduce AdaAct, a novel optimization algorithm that adjusts learning rates according to activation variance. Our method enhances the stability of neuron outputs by incorporating neuron-wise adaptivity during the training process, which subsequently leads to better generalization -- a complementary approach to conventional activation regularization methods. Experimental results demonstrate AdaAct's competitive performance across standard image classification benchmarks. We evaluate AdaAct on CIFAR and ImageNet, comparing it with other state-of-the-art methods. Importantly, AdaAct effective"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.08353","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/2506.08353/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2506.08353","created_at":"2026-07-05T11:18:48.442027+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.08353v1","created_at":"2026-07-05T11:18:48.442027+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.08353","created_at":"2026-07-05T11:18:48.442027+00:00"},{"alias_kind":"pith_short_12","alias_value":"D2DSRQ5B7243","created_at":"2026-07-05T11:18:48.442027+00:00"},{"alias_kind":"pith_short_16","alias_value":"D2DSRQ5B72432AK7","created_at":"2026-07-05T11:18:48.442027+00:00"},{"alias_kind":"pith_short_8","alias_value":"D2DSRQ5B","created_at":"2026-07-05T11:18:48.442027+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/D2DSRQ5B72432AK7DI2UCDZOIA","json":"https://pith.science/pith/D2DSRQ5B72432AK7DI2UCDZOIA.json","graph_json":"https://pith.science/api/pith-number/D2DSRQ5B72432AK7DI2UCDZOIA/graph.json","events_json":"https://pith.science/api/pith-number/D2DSRQ5B72432AK7DI2UCDZOIA/events.json","paper":"https://pith.science/paper/D2DSRQ5B"},"agent_actions":{"view_html":"https://pith.science/pith/D2DSRQ5B72432AK7DI2UCDZOIA","download_json":"https://pith.science/pith/D2DSRQ5B72432AK7DI2UCDZOIA.json","view_paper":"https://pith.science/paper/D2DSRQ5B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.08353&json=true","fetch_graph":"https://pith.science/api/pith-number/D2DSRQ5B72432AK7DI2UCDZOIA/graph.json","fetch_events":"https://pith.science/api/pith-number/D2DSRQ5B72432AK7DI2UCDZOIA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D2DSRQ5B72432AK7DI2UCDZOIA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D2DSRQ5B72432AK7DI2UCDZOIA/action/storage_attestation","attest_author":"https://pith.science/pith/D2DSRQ5B72432AK7DI2UCDZOIA/action/author_attestation","sign_citation":"https://pith.science/pith/D2DSRQ5B72432AK7DI2UCDZOIA/action/citation_signature","submit_replication":"https://pith.science/pith/D2DSRQ5B72432AK7DI2UCDZOIA/action/replication_record"}},"created_at":"2026-07-05T11:18:48.442027+00:00","updated_at":"2026-07-05T11:18:48.442027+00:00"}