{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:N7UXN5S662VFTGZRXOMSEG5EC7","short_pith_number":"pith:N7UXN5S6","canonical_record":{"source":{"id":"2009.11763","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-09-24T15:40:55Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"0511dfc262badc110e98a871ad4c10457c9bfdc97e83db8403135e884a6288e1","abstract_canon_sha256":"a96969e251024cac0c2474e59e88705e168b032c241eee86fb0bfe8f60e0fba1"},"schema_version":"1.0"},"canonical_sha256":"6fe976f65ef6aa599b31bb99221ba417ef669b9cddbc82a297ba927998eacd7f","source":{"kind":"arxiv","id":"2009.11763","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2009.11763","created_at":"2026-07-05T01:37:49Z"},{"alias_kind":"arxiv_version","alias_value":"2009.11763v1","created_at":"2026-07-05T01:37:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.11763","created_at":"2026-07-05T01:37:49Z"},{"alias_kind":"pith_short_12","alias_value":"N7UXN5S662VF","created_at":"2026-07-05T01:37:49Z"},{"alias_kind":"pith_short_16","alias_value":"N7UXN5S662VFTGZR","created_at":"2026-07-05T01:37:49Z"},{"alias_kind":"pith_short_8","alias_value":"N7UXN5S6","created_at":"2026-07-05T01:37:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:N7UXN5S662VFTGZRXOMSEG5EC7","target":"record","payload":{"canonical_record":{"source":{"id":"2009.11763","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-09-24T15:40:55Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"0511dfc262badc110e98a871ad4c10457c9bfdc97e83db8403135e884a6288e1","abstract_canon_sha256":"a96969e251024cac0c2474e59e88705e168b032c241eee86fb0bfe8f60e0fba1"},"schema_version":"1.0"},"canonical_sha256":"6fe976f65ef6aa599b31bb99221ba417ef669b9cddbc82a297ba927998eacd7f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:37:49.687474Z","signature_b64":"w9Gy+azfaqrC5Qlq2QhW0R1DFXac4K3cmtDBOZ4X/IExQ0AYy4t1uypAKjGMV3/nJeorGYBF9CdP8PXr4r/6Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6fe976f65ef6aa599b31bb99221ba417ef669b9cddbc82a297ba927998eacd7f","last_reissued_at":"2026-07-05T01:37:49.686965Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:37:49.686965Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2009.11763","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":"2026-07-05T01:37:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fmAyQnye/4J01kSjkPwsx+ZgcUrK78RBl22wJS3bMKH+YMcJkbIz0Jwg/Vmz2pzAfU3/FF+oTb+eYUMFBGBpBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T19:26:15.442682Z"},"content_sha256":"49b58d99a7ea4ab473c07c50c653bdf8a245193e7079344301f3fa137e88652b","schema_version":"1.0","event_id":"sha256:49b58d99a7ea4ab473c07c50c653bdf8a245193e7079344301f3fa137e88652b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:N7UXN5S662VFTGZRXOMSEG5EC7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unsupervised Transfer Learning for Spatiotemporal Predictive Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Jianmin Wang, Mingsheng Long, Yunbo Wang, Zhiyu Yao","submitted_at":"2020-09-24T15:40:55Z","abstract_excerpt":"This paper explores a new research problem of unsupervised transfer learning across multiple spatiotemporal prediction tasks. Unlike most existing transfer learning methods that focus on fixing the discrepancy between supervised tasks, we study how to transfer knowledge from a zoo of unsupervisedly learned models towards another predictive network. Our motivation is that models from different sources are expected to understand the complex spatiotemporal dynamics from different perspectives, thereby effectively supplementing the new task, even if the task has sufficient training samples. Techni"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.11763","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/2009.11763/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":"2026-07-05T01:37:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pi4WkI+XUJqJq17643B4GP8Lsnv6zQRq69pjkVVvfD8mhHUxAjYPhZBC8QgtaFJCwL86ReOBfOX0YR3dZ8DGCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T19:26:15.468010Z"},"content_sha256":"3ae9b5d56b153ca707dc1c5cad0c4b695b3e12d0427b0a8195e9bbac3be7ea4f","schema_version":"1.0","event_id":"sha256:3ae9b5d56b153ca707dc1c5cad0c4b695b3e12d0427b0a8195e9bbac3be7ea4f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/N7UXN5S662VFTGZRXOMSEG5EC7/bundle.json","state_url":"https://pith.science/pith/N7UXN5S662VFTGZRXOMSEG5EC7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/N7UXN5S662VFTGZRXOMSEG5EC7/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":"2026-08-04T19:26:15Z","links":{"resolver":"https://pith.science/pith/N7UXN5S662VFTGZRXOMSEG5EC7","bundle":"https://pith.science/pith/N7UXN5S662VFTGZRXOMSEG5EC7/bundle.json","state":"https://pith.science/pith/N7UXN5S662VFTGZRXOMSEG5EC7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/N7UXN5S662VFTGZRXOMSEG5EC7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:N7UXN5S662VFTGZRXOMSEG5EC7","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":"a96969e251024cac0c2474e59e88705e168b032c241eee86fb0bfe8f60e0fba1","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-09-24T15:40:55Z","title_canon_sha256":"0511dfc262badc110e98a871ad4c10457c9bfdc97e83db8403135e884a6288e1"},"schema_version":"1.0","source":{"id":"2009.11763","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2009.11763","created_at":"2026-07-05T01:37:49Z"},{"alias_kind":"arxiv_version","alias_value":"2009.11763v1","created_at":"2026-07-05T01:37:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.11763","created_at":"2026-07-05T01:37:49Z"},{"alias_kind":"pith_short_12","alias_value":"N7UXN5S662VF","created_at":"2026-07-05T01:37:49Z"},{"alias_kind":"pith_short_16","alias_value":"N7UXN5S662VFTGZR","created_at":"2026-07-05T01:37:49Z"},{"alias_kind":"pith_short_8","alias_value":"N7UXN5S6","created_at":"2026-07-05T01:37:49Z"}],"graph_snapshots":[{"event_id":"sha256:3ae9b5d56b153ca707dc1c5cad0c4b695b3e12d0427b0a8195e9bbac3be7ea4f","target":"graph","created_at":"2026-07-05T01:37:49Z","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/2009.11763/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper explores a new research problem of unsupervised transfer learning across multiple spatiotemporal prediction tasks. Unlike most existing transfer learning methods that focus on fixing the discrepancy between supervised tasks, we study how to transfer knowledge from a zoo of unsupervisedly learned models towards another predictive network. Our motivation is that models from different sources are expected to understand the complex spatiotemporal dynamics from different perspectives, thereby effectively supplementing the new task, even if the task has sufficient training samples. Techni","authors_text":"Jianmin Wang, Mingsheng Long, Yunbo Wang, Zhiyu Yao","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-09-24T15:40:55Z","title":"Unsupervised Transfer Learning for Spatiotemporal Predictive Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.11763","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":"sha256:49b58d99a7ea4ab473c07c50c653bdf8a245193e7079344301f3fa137e88652b","target":"record","created_at":"2026-07-05T01:37:49Z","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":"a96969e251024cac0c2474e59e88705e168b032c241eee86fb0bfe8f60e0fba1","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-09-24T15:40:55Z","title_canon_sha256":"0511dfc262badc110e98a871ad4c10457c9bfdc97e83db8403135e884a6288e1"},"schema_version":"1.0","source":{"id":"2009.11763","kind":"arxiv","version":1}},"canonical_sha256":"6fe976f65ef6aa599b31bb99221ba417ef669b9cddbc82a297ba927998eacd7f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6fe976f65ef6aa599b31bb99221ba417ef669b9cddbc82a297ba927998eacd7f","first_computed_at":"2026-07-05T01:37:49.686965Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:37:49.686965Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"w9Gy+azfaqrC5Qlq2QhW0R1DFXac4K3cmtDBOZ4X/IExQ0AYy4t1uypAKjGMV3/nJeorGYBF9CdP8PXr4r/6Dg==","signature_status":"signed_v1","signed_at":"2026-07-05T01:37:49.687474Z","signed_message":"canonical_sha256_bytes"},"source_id":"2009.11763","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:49b58d99a7ea4ab473c07c50c653bdf8a245193e7079344301f3fa137e88652b","sha256:3ae9b5d56b153ca707dc1c5cad0c4b695b3e12d0427b0a8195e9bbac3be7ea4f"],"state_sha256":"fe7cc81d0a8c4525badf45990c48f4f26b4ff4f7fd16cd54fff7946eaa13b6c8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fmcgmQTBBqqtrUyDyrJ4n46x0yZCCRctU5ZjCmN/lbdTq+1HHz38QqZVzTaJJrWJSlfx/sTHkVQStluw6JMJBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T19:26:15.602884Z","bundle_sha256":"04811bfc37c0b783a7b8094a5c9a91dd0d1b4585af068d6611c1bfa1b5883a70"}}