{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:3EYKFGOLU3JWJR5QH5VBJUHEFF","short_pith_number":"pith:3EYKFGOL","canonical_record":{"source":{"id":"2101.09819","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-01-24T22:57:19Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"ef17efbbbb6d5b2423fc9e0f7e1225608eb166819adfe40eabc13f06a12b8621","abstract_canon_sha256":"7090d09f1900bb0e2e36631ff440dc6730c496c8cf3cb5cb4e1395c934039b78"},"schema_version":"1.0"},"canonical_sha256":"d930a299cba6d364c7b03f6a14d0e42943bb8f69ae01e3618251823e87c774b4","source":{"kind":"arxiv","id":"2101.09819","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2101.09819","created_at":"2026-07-05T02:09:22Z"},{"alias_kind":"arxiv_version","alias_value":"2101.09819v1","created_at":"2026-07-05T02:09:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.09819","created_at":"2026-07-05T02:09:22Z"},{"alias_kind":"pith_short_12","alias_value":"3EYKFGOLU3JW","created_at":"2026-07-05T02:09:22Z"},{"alias_kind":"pith_short_16","alias_value":"3EYKFGOLU3JWJR5Q","created_at":"2026-07-05T02:09:22Z"},{"alias_kind":"pith_short_8","alias_value":"3EYKFGOL","created_at":"2026-07-05T02:09:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:3EYKFGOLU3JWJR5QH5VBJUHEFF","target":"record","payload":{"canonical_record":{"source":{"id":"2101.09819","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-01-24T22:57:19Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"ef17efbbbb6d5b2423fc9e0f7e1225608eb166819adfe40eabc13f06a12b8621","abstract_canon_sha256":"7090d09f1900bb0e2e36631ff440dc6730c496c8cf3cb5cb4e1395c934039b78"},"schema_version":"1.0"},"canonical_sha256":"d930a299cba6d364c7b03f6a14d0e42943bb8f69ae01e3618251823e87c774b4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:09:22.821205Z","signature_b64":"AbTzCnXAqvi1ARsr3/XHsguBUlxybqfb0g656h0vYRkxpzBTa8DPiDNtb8ceVxzYBg8T1vDyT9P1qqzBS/ncAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d930a299cba6d364c7b03f6a14d0e42943bb8f69ae01e3618251823e87c774b4","last_reissued_at":"2026-07-05T02:09:22.820772Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:09:22.820772Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2101.09819","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":"2026-07-05T02:09:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ve5XBOxXjUh8GRWEFbZVpaOwFc2W7lQCdym+htIW8q8Z94YO2g7EIQHw09xEPt9htVMj2GAX6bJ2X9jeaKCrAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-24T02:16:38.909857Z"},"content_sha256":"85552bf07ddf30d4632abf3e88127f6e3c8b005ffcd496e8bdf5636e307cfc8a","schema_version":"1.0","event_id":"sha256:85552bf07ddf30d4632abf3e88127f6e3c8b005ffcd496e8bdf5636e307cfc8a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:3EYKFGOLU3JWJR5QH5VBJUHEFF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Meta-Regularization by Enforcing Mutual-Exclusiveness","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Edwin Pan, Pankaj Rajak, Shubham Shrivastava","submitted_at":"2021-01-24T22:57:19Z","abstract_excerpt":"Meta-learning models have two objectives. First, they need to be able to make predictions over a range of task distributions while utilizing only a small amount of training data. Second, they also need to adapt to new novel unseen tasks at meta-test time again by using only a small amount of training data from that task. It is the second objective where meta-learning models fail for non-mutually exclusive tasks due to task overfitting. Given that guaranteeing mutually exclusive tasks is often difficult, there is a significant need for regularization methods that can help reduce the impact of t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.09819","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/2101.09819/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":"2026-07-05T02:09:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YJFddFKa8/tUjm13+74yx+KTnX5J8eYp3sj35HJaFFkrplBxqISI5Yw0+VOSVJsedxtk8n77jTDlb8r/c6wpAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-24T02:16:38.910349Z"},"content_sha256":"702529540b92dbc5c2219e8da6ad448c9f2e731bda6528f4f1ede0f8167a1c2b","schema_version":"1.0","event_id":"sha256:702529540b92dbc5c2219e8da6ad448c9f2e731bda6528f4f1ede0f8167a1c2b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3EYKFGOLU3JWJR5QH5VBJUHEFF/bundle.json","state_url":"https://pith.science/pith/3EYKFGOLU3JWJR5QH5VBJUHEFF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3EYKFGOLU3JWJR5QH5VBJUHEFF/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":"2026-08-24T02:16:38Z","links":{"resolver":"https://pith.science/pith/3EYKFGOLU3JWJR5QH5VBJUHEFF","bundle":"https://pith.science/pith/3EYKFGOLU3JWJR5QH5VBJUHEFF/bundle.json","state":"https://pith.science/pith/3EYKFGOLU3JWJR5QH5VBJUHEFF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3EYKFGOLU3JWJR5QH5VBJUHEFF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:3EYKFGOLU3JWJR5QH5VBJUHEFF","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":"7090d09f1900bb0e2e36631ff440dc6730c496c8cf3cb5cb4e1395c934039b78","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-01-24T22:57:19Z","title_canon_sha256":"ef17efbbbb6d5b2423fc9e0f7e1225608eb166819adfe40eabc13f06a12b8621"},"schema_version":"1.0","source":{"id":"2101.09819","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2101.09819","created_at":"2026-07-05T02:09:22Z"},{"alias_kind":"arxiv_version","alias_value":"2101.09819v1","created_at":"2026-07-05T02:09:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.09819","created_at":"2026-07-05T02:09:22Z"},{"alias_kind":"pith_short_12","alias_value":"3EYKFGOLU3JW","created_at":"2026-07-05T02:09:22Z"},{"alias_kind":"pith_short_16","alias_value":"3EYKFGOLU3JWJR5Q","created_at":"2026-07-05T02:09:22Z"},{"alias_kind":"pith_short_8","alias_value":"3EYKFGOL","created_at":"2026-07-05T02:09:22Z"}],"graph_snapshots":[{"event_id":"sha256:702529540b92dbc5c2219e8da6ad448c9f2e731bda6528f4f1ede0f8167a1c2b","target":"graph","created_at":"2026-07-05T02:09:22Z","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/2101.09819/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Meta-learning models have two objectives. First, they need to be able to make predictions over a range of task distributions while utilizing only a small amount of training data. Second, they also need to adapt to new novel unseen tasks at meta-test time again by using only a small amount of training data from that task. It is the second objective where meta-learning models fail for non-mutually exclusive tasks due to task overfitting. Given that guaranteeing mutually exclusive tasks is often difficult, there is a significant need for regularization methods that can help reduce the impact of t","authors_text":"Edwin Pan, Pankaj Rajak, Shubham Shrivastava","cross_cats":["cs.AI","cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-01-24T22:57:19Z","title":"Meta-Regularization by Enforcing Mutual-Exclusiveness"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.09819","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":"sha256:85552bf07ddf30d4632abf3e88127f6e3c8b005ffcd496e8bdf5636e307cfc8a","target":"record","created_at":"2026-07-05T02:09:22Z","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":"7090d09f1900bb0e2e36631ff440dc6730c496c8cf3cb5cb4e1395c934039b78","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-01-24T22:57:19Z","title_canon_sha256":"ef17efbbbb6d5b2423fc9e0f7e1225608eb166819adfe40eabc13f06a12b8621"},"schema_version":"1.0","source":{"id":"2101.09819","kind":"arxiv","version":1}},"canonical_sha256":"d930a299cba6d364c7b03f6a14d0e42943bb8f69ae01e3618251823e87c774b4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d930a299cba6d364c7b03f6a14d0e42943bb8f69ae01e3618251823e87c774b4","first_computed_at":"2026-07-05T02:09:22.820772Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:09:22.820772Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AbTzCnXAqvi1ARsr3/XHsguBUlxybqfb0g656h0vYRkxpzBTa8DPiDNtb8ceVxzYBg8T1vDyT9P1qqzBS/ncAA==","signature_status":"signed_v1","signed_at":"2026-07-05T02:09:22.821205Z","signed_message":"canonical_sha256_bytes"},"source_id":"2101.09819","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:85552bf07ddf30d4632abf3e88127f6e3c8b005ffcd496e8bdf5636e307cfc8a","sha256:702529540b92dbc5c2219e8da6ad448c9f2e731bda6528f4f1ede0f8167a1c2b"],"state_sha256":"670f5c6a61ea6a672c1d163d9aa09e24f9722743dbee16d8959e7232ecd6b40c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vVt4fff7HS1BiIOO6GqfKrXfDhccVCO27J9YVvQkAeUz8VThZbCI5AdfbG8diVMYmVeB99Zg/w6bB8pcy8UQCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-24T02:16:38.914187Z","bundle_sha256":"951d9f22abb1c1e56a9f525414918d0e6accadb4ba446ef2d8218ebe0d6719f6"}}