{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:WVHDQIASNHGSFPAXSN6LVGPUK2","short_pith_number":"pith:WVHDQIAS","canonical_record":{"source":{"id":"1908.02984","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-08-08T09:21:21Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"c9be5d67fca4dbfecb957a2e9046771b19487a3b8b8befa9049d1dfe56c0da29","abstract_canon_sha256":"73a2714ca619c7437030eccd45e605fc93b3398805760693b390607b227ecd03"},"schema_version":"1.0"},"canonical_sha256":"b54e38201269cd22bc17937cba99f4569f9241e2bedd0cce9c7d9e420004fcf5","source":{"kind":"arxiv","id":"1908.02984","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.02984","created_at":"2026-07-05T00:13:52Z"},{"alias_kind":"arxiv_version","alias_value":"1908.02984v2","created_at":"2026-07-05T00:13:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.02984","created_at":"2026-07-05T00:13:52Z"},{"alias_kind":"pith_short_12","alias_value":"WVHDQIASNHGS","created_at":"2026-07-05T00:13:52Z"},{"alias_kind":"pith_short_16","alias_value":"WVHDQIASNHGSFPAX","created_at":"2026-07-05T00:13:52Z"},{"alias_kind":"pith_short_8","alias_value":"WVHDQIAS","created_at":"2026-07-05T00:13:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:WVHDQIASNHGSFPAXSN6LVGPUK2","target":"record","payload":{"canonical_record":{"source":{"id":"1908.02984","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-08-08T09:21:21Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"c9be5d67fca4dbfecb957a2e9046771b19487a3b8b8befa9049d1dfe56c0da29","abstract_canon_sha256":"73a2714ca619c7437030eccd45e605fc93b3398805760693b390607b227ecd03"},"schema_version":"1.0"},"canonical_sha256":"b54e38201269cd22bc17937cba99f4569f9241e2bedd0cce9c7d9e420004fcf5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:13:52.950862Z","signature_b64":"AsZHCQJ1tBCO2ga/W0wEmzjsSpIn4dFw0b02MFEcrevrXbzTDGytwTNRbLqNkP+ogDW+SVO5Mvqc3YcoHSs5Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b54e38201269cd22bc17937cba99f4569f9241e2bedd0cce9c7d9e420004fcf5","last_reissued_at":"2026-07-05T00:13:52.950467Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:13:52.950467Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1908.02984","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":"2026-07-05T00:13:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wQGljxSUI9kUP5taNvY3n6RZBLhWUovtYd7MmDyHdze3J0H7J/XxONrDkN5gbN3EhAisWIErfGqKYakm6DWACQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T03:23:26.443699Z"},"content_sha256":"a5fce50f872b3abe0bfb3bc7e1a9317e988853add7874f69b1cb46b44b07c1da","schema_version":"1.0","event_id":"sha256:a5fce50f872b3abe0bfb3bc7e1a9317e988853add7874f69b1cb46b44b07c1da"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:WVHDQIASNHGSFPAXSN6LVGPUK2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Continual Learning by Asymmetric Loss Approximation with Single-Side Overestimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Bohyung Han, Dongmin Park, Kyoung Mu Lee, Seokil Hong","submitted_at":"2019-08-08T09:21:21Z","abstract_excerpt":"Catastrophic forgetting is a critical challenge in training deep neural networks. Although continual learning has been investigated as a countermeasure to the problem, it often suffers from the requirements of additional network components and the limited scalability to a large number of tasks. We propose a novel approach to continual learning by approximating a true loss function using an asymmetric quadratic function with one of its sides overestimated. Our algorithm is motivated by the empirical observation that the network parameter updates affect the target loss functions asymmetrically. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.02984","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/1908.02984/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":"2026-07-05T00:13:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EIU7iOkUFh/3zk8dBLzALeDL3PcXCoT1M+xDBaLp7nfz3S5TDgSSD5sVbTvfFagcXH4G6DhtwTItVwcvQKXMDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T03:23:26.444209Z"},"content_sha256":"bb251d6ff7f7c71bf6fd0db3e178b54e7ab5c3bc35e7c237a60492b8f30c6761","schema_version":"1.0","event_id":"sha256:bb251d6ff7f7c71bf6fd0db3e178b54e7ab5c3bc35e7c237a60492b8f30c6761"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WVHDQIASNHGSFPAXSN6LVGPUK2/bundle.json","state_url":"https://pith.science/pith/WVHDQIASNHGSFPAXSN6LVGPUK2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WVHDQIASNHGSFPAXSN6LVGPUK2/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":"2026-08-16T03:23:26Z","links":{"resolver":"https://pith.science/pith/WVHDQIASNHGSFPAXSN6LVGPUK2","bundle":"https://pith.science/pith/WVHDQIASNHGSFPAXSN6LVGPUK2/bundle.json","state":"https://pith.science/pith/WVHDQIASNHGSFPAXSN6LVGPUK2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WVHDQIASNHGSFPAXSN6LVGPUK2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:WVHDQIASNHGSFPAXSN6LVGPUK2","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":"73a2714ca619c7437030eccd45e605fc93b3398805760693b390607b227ecd03","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-08-08T09:21:21Z","title_canon_sha256":"c9be5d67fca4dbfecb957a2e9046771b19487a3b8b8befa9049d1dfe56c0da29"},"schema_version":"1.0","source":{"id":"1908.02984","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.02984","created_at":"2026-07-05T00:13:52Z"},{"alias_kind":"arxiv_version","alias_value":"1908.02984v2","created_at":"2026-07-05T00:13:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.02984","created_at":"2026-07-05T00:13:52Z"},{"alias_kind":"pith_short_12","alias_value":"WVHDQIASNHGS","created_at":"2026-07-05T00:13:52Z"},{"alias_kind":"pith_short_16","alias_value":"WVHDQIASNHGSFPAX","created_at":"2026-07-05T00:13:52Z"},{"alias_kind":"pith_short_8","alias_value":"WVHDQIAS","created_at":"2026-07-05T00:13:52Z"}],"graph_snapshots":[{"event_id":"sha256:bb251d6ff7f7c71bf6fd0db3e178b54e7ab5c3bc35e7c237a60492b8f30c6761","target":"graph","created_at":"2026-07-05T00:13:52Z","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/1908.02984/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Catastrophic forgetting is a critical challenge in training deep neural networks. Although continual learning has been investigated as a countermeasure to the problem, it often suffers from the requirements of additional network components and the limited scalability to a large number of tasks. We propose a novel approach to continual learning by approximating a true loss function using an asymmetric quadratic function with one of its sides overestimated. Our algorithm is motivated by the empirical observation that the network parameter updates affect the target loss functions asymmetrically. ","authors_text":"Bohyung Han, Dongmin Park, Kyoung Mu Lee, Seokil Hong","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-08-08T09:21:21Z","title":"Continual Learning by Asymmetric Loss Approximation with Single-Side Overestimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.02984","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:a5fce50f872b3abe0bfb3bc7e1a9317e988853add7874f69b1cb46b44b07c1da","target":"record","created_at":"2026-07-05T00:13:52Z","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":"73a2714ca619c7437030eccd45e605fc93b3398805760693b390607b227ecd03","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-08-08T09:21:21Z","title_canon_sha256":"c9be5d67fca4dbfecb957a2e9046771b19487a3b8b8befa9049d1dfe56c0da29"},"schema_version":"1.0","source":{"id":"1908.02984","kind":"arxiv","version":2}},"canonical_sha256":"b54e38201269cd22bc17937cba99f4569f9241e2bedd0cce9c7d9e420004fcf5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b54e38201269cd22bc17937cba99f4569f9241e2bedd0cce9c7d9e420004fcf5","first_computed_at":"2026-07-05T00:13:52.950467Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:13:52.950467Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AsZHCQJ1tBCO2ga/W0wEmzjsSpIn4dFw0b02MFEcrevrXbzTDGytwTNRbLqNkP+ogDW+SVO5Mvqc3YcoHSs5Dw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:13:52.950862Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.02984","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a5fce50f872b3abe0bfb3bc7e1a9317e988853add7874f69b1cb46b44b07c1da","sha256:bb251d6ff7f7c71bf6fd0db3e178b54e7ab5c3bc35e7c237a60492b8f30c6761"],"state_sha256":"826f90f1631f7e3f7b8476b5515c239d9539eaf8f1a3931c1d34e3d55e095fab"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NbhLwVISQChscxJkY3QXGj9XfACuYfc/4URAU9eyX5DqJnfC/cQ/DtKlCcxfbYyO9PDTKJ2rOcAJmUuBGCdWAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T03:23:26.448724Z","bundle_sha256":"dd2f523bd0fbde076dfa7dc03ee746aac08fa356f25dfb6d41022242c8b42398"}}