{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:ZZTL6A5ECAZYHGEFDFEADGZF3L","short_pith_number":"pith:ZZTL6A5E","canonical_record":{"source":{"id":"1905.03980","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2019-05-10T07:30:53Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"8cea1015db123b98b79b4fb8d8016418772db1bbf09e5d34ab7818a40516c113","abstract_canon_sha256":"1647f48bbee9d1fbcb1b7cc626e268acd55d010a20914e135e44857df0dbf7e2"},"schema_version":"1.0"},"canonical_sha256":"ce66bf03a410338398851948019b25dadb29b2f7d51f6e96a24eea4a12ee6290","source":{"kind":"arxiv","id":"1905.03980","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.03980","created_at":"2026-05-17T23:46:34Z"},{"alias_kind":"arxiv_version","alias_value":"1905.03980v1","created_at":"2026-05-17T23:46:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.03980","created_at":"2026-05-17T23:46:34Z"},{"alias_kind":"pith_short_12","alias_value":"ZZTL6A5ECAZY","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"ZZTL6A5ECAZYHGEF","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"ZZTL6A5E","created_at":"2026-05-18T12:33:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:ZZTL6A5ECAZYHGEFDFEADGZF3L","target":"record","payload":{"canonical_record":{"source":{"id":"1905.03980","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2019-05-10T07:30:53Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"8cea1015db123b98b79b4fb8d8016418772db1bbf09e5d34ab7818a40516c113","abstract_canon_sha256":"1647f48bbee9d1fbcb1b7cc626e268acd55d010a20914e135e44857df0dbf7e2"},"schema_version":"1.0"},"canonical_sha256":"ce66bf03a410338398851948019b25dadb29b2f7d51f6e96a24eea4a12ee6290","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:34.978152Z","signature_b64":"MomjV+Af0mqjpLezJSexpkT66/rdp7rlPeoSbA6C8ZAfEtCZgSWmam1IY0zc3QMcc0nqbqE9qo0OPQlrV4YFAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ce66bf03a410338398851948019b25dadb29b2f7d51f6e96a24eea4a12ee6290","last_reissued_at":"2026-05-17T23:46:34.977672Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:34.977672Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.03980","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:46:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0HsQvclRFep/LmDpkdeJqFF+6Q03dOU+8jEtkxz3TyJCV40g0jbc5kvXvM3epbng3FcYvlPTH/I8Zi67yDp3AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T06:37:08.493104Z"},"content_sha256":"f16df7dfd20eb5c9d0a50610af5aba0419ef5e5d1a12b7f0eed8813c2918b7aa","schema_version":"1.0","event_id":"sha256:f16df7dfd20eb5c9d0a50610af5aba0419ef5e5d1a12b7f0eed8813c2918b7aa"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:ZZTL6A5ECAZYHGEFDFEADGZF3L","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bayesian Optimized Continual Learning with Attention Mechanism","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Jin Ma, Ju Xu, Zhanxing Zhu","submitted_at":"2019-05-10T07:30:53Z","abstract_excerpt":"Though neural networks have achieved much progress in various applications, it is still highly challenging for them to learn from a continuous stream of tasks without forgetting. Continual learning, a new learning paradigm, aims to solve this issue. In this work, we propose a new model for continual learning, called Bayesian Optimized Continual Learning with Attention Mechanism (BOCL) that dynamically expands the network capacity upon the arrival of new tasks by Bayesian optimization and selectively utilizes previous knowledge (e.g. feature maps of previous tasks) via attention mechanism. Our "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.03980","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":""},"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":"2026-05-17T23:46:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"G3LYJxjzWPGI4fX0riFsCaNa+Ac71gStNCTtmKx+TAXvOnIEXNilpw5EcdnOP8eJVZFVzmcprZ4NTHo1Rq88Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T06:37:08.493467Z"},"content_sha256":"1f553d73af5e094ea08a1e081a56bbec71c48457e5e87bcf66ed4203bc41965d","schema_version":"1.0","event_id":"sha256:1f553d73af5e094ea08a1e081a56bbec71c48457e5e87bcf66ed4203bc41965d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZZTL6A5ECAZYHGEFDFEADGZF3L/bundle.json","state_url":"https://pith.science/pith/ZZTL6A5ECAZYHGEFDFEADGZF3L/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZZTL6A5ECAZYHGEFDFEADGZF3L/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":"2026-07-04T06:37:08Z","links":{"resolver":"https://pith.science/pith/ZZTL6A5ECAZYHGEFDFEADGZF3L","bundle":"https://pith.science/pith/ZZTL6A5ECAZYHGEFDFEADGZF3L/bundle.json","state":"https://pith.science/pith/ZZTL6A5ECAZYHGEFDFEADGZF3L/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZZTL6A5ECAZYHGEFDFEADGZF3L/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:ZZTL6A5ECAZYHGEFDFEADGZF3L","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":"1647f48bbee9d1fbcb1b7cc626e268acd55d010a20914e135e44857df0dbf7e2","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2019-05-10T07:30:53Z","title_canon_sha256":"8cea1015db123b98b79b4fb8d8016418772db1bbf09e5d34ab7818a40516c113"},"schema_version":"1.0","source":{"id":"1905.03980","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.03980","created_at":"2026-05-17T23:46:34Z"},{"alias_kind":"arxiv_version","alias_value":"1905.03980v1","created_at":"2026-05-17T23:46:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.03980","created_at":"2026-05-17T23:46:34Z"},{"alias_kind":"pith_short_12","alias_value":"ZZTL6A5ECAZY","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"ZZTL6A5ECAZYHGEF","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"ZZTL6A5E","created_at":"2026-05-18T12:33:33Z"}],"graph_snapshots":[{"event_id":"sha256:1f553d73af5e094ea08a1e081a56bbec71c48457e5e87bcf66ed4203bc41965d","target":"graph","created_at":"2026-05-17T23:46:34Z","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"},"paper":{"abstract_excerpt":"Though neural networks have achieved much progress in various applications, it is still highly challenging for them to learn from a continuous stream of tasks without forgetting. Continual learning, a new learning paradigm, aims to solve this issue. In this work, we propose a new model for continual learning, called Bayesian Optimized Continual Learning with Attention Mechanism (BOCL) that dynamically expands the network capacity upon the arrival of new tasks by Bayesian optimization and selectively utilizes previous knowledge (e.g. feature maps of previous tasks) via attention mechanism. Our ","authors_text":"Jin Ma, Ju Xu, Zhanxing Zhu","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2019-05-10T07:30:53Z","title":"Bayesian Optimized Continual Learning with Attention Mechanism"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.03980","kind":"arxiv","version":1},"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:f16df7dfd20eb5c9d0a50610af5aba0419ef5e5d1a12b7f0eed8813c2918b7aa","target":"record","created_at":"2026-05-17T23:46:34Z","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":"1647f48bbee9d1fbcb1b7cc626e268acd55d010a20914e135e44857df0dbf7e2","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2019-05-10T07:30:53Z","title_canon_sha256":"8cea1015db123b98b79b4fb8d8016418772db1bbf09e5d34ab7818a40516c113"},"schema_version":"1.0","source":{"id":"1905.03980","kind":"arxiv","version":1}},"canonical_sha256":"ce66bf03a410338398851948019b25dadb29b2f7d51f6e96a24eea4a12ee6290","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ce66bf03a410338398851948019b25dadb29b2f7d51f6e96a24eea4a12ee6290","first_computed_at":"2026-05-17T23:46:34.977672Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:46:34.977672Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MomjV+Af0mqjpLezJSexpkT66/rdp7rlPeoSbA6C8ZAfEtCZgSWmam1IY0zc3QMcc0nqbqE9qo0OPQlrV4YFAQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:46:34.978152Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.03980","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f16df7dfd20eb5c9d0a50610af5aba0419ef5e5d1a12b7f0eed8813c2918b7aa","sha256:1f553d73af5e094ea08a1e081a56bbec71c48457e5e87bcf66ed4203bc41965d"],"state_sha256":"11b9dd0a4665f3ee6f37b97bcac610d03ea8784e0deaa66ca86b9812dba178c8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"L4wTuShp2xuMbBqathzHQ5Ibk2v8H+f9m1XGz3XDyfKRUG6Y5do4O0oOHc/RO9HfpU8lZ5MB6IeVCew/e8tnDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-04T06:37:08.495450Z","bundle_sha256":"4f999d5726c3658d2c153dd57154820d8460edd6faef2589020f0835af215a67"}}