{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:QAQPKYT5ZCUSPX2PRTXTSG2P4Z","short_pith_number":"pith:QAQPKYT5","canonical_record":{"source":{"id":"1809.00758","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-04T00:52:25Z","cross_cats_sorted":["cs.CV","cs.SD","eess.AS","stat.ML"],"title_canon_sha256":"88403e24ac4607da7530b6b4ec135bd3e89d3bd93e7055513f015120b33872a9","abstract_canon_sha256":"80c99f27a608ed7f183c1541e37d37ab5a48829928a18de389226eefda37dd1f"},"schema_version":"1.0"},"canonical_sha256":"8020f5627dc8a927df4f8cef391b4fe6786bada93e6032b9710d80e1ad8aa86f","source":{"kind":"arxiv","id":"1809.00758","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.00758","created_at":"2026-05-18T00:04:20Z"},{"alias_kind":"arxiv_version","alias_value":"1809.00758v3","created_at":"2026-05-18T00:04:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.00758","created_at":"2026-05-18T00:04:20Z"},{"alias_kind":"pith_short_12","alias_value":"QAQPKYT5ZCUS","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_16","alias_value":"QAQPKYT5ZCUSPX2P","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_8","alias_value":"QAQPKYT5","created_at":"2026-05-18T12:32:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:QAQPKYT5ZCUSPX2PRTXTSG2P4Z","target":"record","payload":{"canonical_record":{"source":{"id":"1809.00758","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-04T00:52:25Z","cross_cats_sorted":["cs.CV","cs.SD","eess.AS","stat.ML"],"title_canon_sha256":"88403e24ac4607da7530b6b4ec135bd3e89d3bd93e7055513f015120b33872a9","abstract_canon_sha256":"80c99f27a608ed7f183c1541e37d37ab5a48829928a18de389226eefda37dd1f"},"schema_version":"1.0"},"canonical_sha256":"8020f5627dc8a927df4f8cef391b4fe6786bada93e6032b9710d80e1ad8aa86f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:04:20.028386Z","signature_b64":"V7tkIBYbHYpRPA5Wj+sN1gFudgdIfherxSZkzjV0zGYcq7gdhEdAEV74M9aaVkhkNFcY8O1Czmw78ThxHJGtDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8020f5627dc8a927df4f8cef391b4fe6786bada93e6032b9710d80e1ad8aa86f","last_reissued_at":"2026-05-18T00:04:20.027446Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:04:20.027446Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1809.00758","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":"2026-05-18T00:04:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YpPMN4hGmLYENEhvb6+LXurAB+P6LG9HGeaBaK1x6kIZZADwioVa3J5F4U+nnbosTwLrplslaC70N1cC5sLiBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T21:27:17.255115Z"},"content_sha256":"5981259d598640296e7c05928db5575c73af76f39833ba946105225fafeca9cb","schema_version":"1.0","event_id":"sha256:5981259d598640296e7c05928db5575c73af76f39833ba946105225fafeca9cb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:QAQPKYT5ZCUSPX2PRTXTSG2P4Z","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"End-to-end Multimodal Emotion and Gender Recognition with Dynamic Joint Loss Weights","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.SD","eess.AS","stat.ML"],"primary_cat":"cs.LG","authors_text":"June-Woo Kim, Myungsu Chae, Soo-Young Lee, Tae-Ho Kim, Young Hoon Shin","submitted_at":"2018-09-04T00:52:25Z","abstract_excerpt":"Multi-task learning is a method for improving the generalizability of multiple tasks. In order to perform multiple classification tasks with one neural network model, the losses of each task should be combined. Previous studies have mostly focused on multiple prediction tasks using joint loss with static weights for training models, choosing the weights between tasks without making sufficient considerations by setting them uniformly or empirically. In this study, we propose a method to calculate joint loss using dynamic weights to improve the total performance, instead of the individual perfor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.00758","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":""},"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":"2026-05-18T00:04:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HCJn/YhgyC+DGdcl6ZHDEFJtKuX8j56e1sDNWdJYz4iXWYsBdgDeOmkdXdnni89qXhAAnWQOOpVUEkbQt3xeBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T21:27:17.255764Z"},"content_sha256":"232b96d02f1cb607b6a9dd6968f273de1953598def49570bc687dbfad51cb4ff","schema_version":"1.0","event_id":"sha256:232b96d02f1cb607b6a9dd6968f273de1953598def49570bc687dbfad51cb4ff"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QAQPKYT5ZCUSPX2PRTXTSG2P4Z/bundle.json","state_url":"https://pith.science/pith/QAQPKYT5ZCUSPX2PRTXTSG2P4Z/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QAQPKYT5ZCUSPX2PRTXTSG2P4Z/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":"2026-06-06T21:27:17Z","links":{"resolver":"https://pith.science/pith/QAQPKYT5ZCUSPX2PRTXTSG2P4Z","bundle":"https://pith.science/pith/QAQPKYT5ZCUSPX2PRTXTSG2P4Z/bundle.json","state":"https://pith.science/pith/QAQPKYT5ZCUSPX2PRTXTSG2P4Z/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QAQPKYT5ZCUSPX2PRTXTSG2P4Z/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:QAQPKYT5ZCUSPX2PRTXTSG2P4Z","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":"80c99f27a608ed7f183c1541e37d37ab5a48829928a18de389226eefda37dd1f","cross_cats_sorted":["cs.CV","cs.SD","eess.AS","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-04T00:52:25Z","title_canon_sha256":"88403e24ac4607da7530b6b4ec135bd3e89d3bd93e7055513f015120b33872a9"},"schema_version":"1.0","source":{"id":"1809.00758","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.00758","created_at":"2026-05-18T00:04:20Z"},{"alias_kind":"arxiv_version","alias_value":"1809.00758v3","created_at":"2026-05-18T00:04:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.00758","created_at":"2026-05-18T00:04:20Z"},{"alias_kind":"pith_short_12","alias_value":"QAQPKYT5ZCUS","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_16","alias_value":"QAQPKYT5ZCUSPX2P","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_8","alias_value":"QAQPKYT5","created_at":"2026-05-18T12:32:46Z"}],"graph_snapshots":[{"event_id":"sha256:232b96d02f1cb607b6a9dd6968f273de1953598def49570bc687dbfad51cb4ff","target":"graph","created_at":"2026-05-18T00:04:20Z","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":"Multi-task learning is a method for improving the generalizability of multiple tasks. In order to perform multiple classification tasks with one neural network model, the losses of each task should be combined. Previous studies have mostly focused on multiple prediction tasks using joint loss with static weights for training models, choosing the weights between tasks without making sufficient considerations by setting them uniformly or empirically. In this study, we propose a method to calculate joint loss using dynamic weights to improve the total performance, instead of the individual perfor","authors_text":"June-Woo Kim, Myungsu Chae, Soo-Young Lee, Tae-Ho Kim, Young Hoon Shin","cross_cats":["cs.CV","cs.SD","eess.AS","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-04T00:52:25Z","title":"End-to-end Multimodal Emotion and Gender Recognition with Dynamic Joint Loss Weights"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.00758","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":"sha256:5981259d598640296e7c05928db5575c73af76f39833ba946105225fafeca9cb","target":"record","created_at":"2026-05-18T00:04:20Z","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":"80c99f27a608ed7f183c1541e37d37ab5a48829928a18de389226eefda37dd1f","cross_cats_sorted":["cs.CV","cs.SD","eess.AS","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-04T00:52:25Z","title_canon_sha256":"88403e24ac4607da7530b6b4ec135bd3e89d3bd93e7055513f015120b33872a9"},"schema_version":"1.0","source":{"id":"1809.00758","kind":"arxiv","version":3}},"canonical_sha256":"8020f5627dc8a927df4f8cef391b4fe6786bada93e6032b9710d80e1ad8aa86f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8020f5627dc8a927df4f8cef391b4fe6786bada93e6032b9710d80e1ad8aa86f","first_computed_at":"2026-05-18T00:04:20.027446Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:04:20.027446Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"V7tkIBYbHYpRPA5Wj+sN1gFudgdIfherxSZkzjV0zGYcq7gdhEdAEV74M9aaVkhkNFcY8O1Czmw78ThxHJGtDw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:04:20.028386Z","signed_message":"canonical_sha256_bytes"},"source_id":"1809.00758","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5981259d598640296e7c05928db5575c73af76f39833ba946105225fafeca9cb","sha256:232b96d02f1cb607b6a9dd6968f273de1953598def49570bc687dbfad51cb4ff"],"state_sha256":"d6781345b45adf7fc3b0d1c3fd69a2e6369db5fc8c6361ddaca131c14dbf8d7e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5/evi206u5ezTICkbYQZsuu/CWGeZGaKJPwbgaRixbZUFzZ6YQG5pUDCSX/0fj6RkkrpbGKWru1IFxlo9T3NBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T21:27:17.258905Z","bundle_sha256":"e8c3b64a88b84ee74d879633a6f56700ae36e6fc722ca12ab2204dc37bad7a48"}}