{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:OVMLBTIGV2TLBS6UJL66YT74PE","short_pith_number":"pith:OVMLBTIG","canonical_record":{"source":{"id":"1804.00222","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-31T22:44:28Z","cross_cats_sorted":["cs.NE","stat.ML"],"title_canon_sha256":"c2333451cdd43feedfd80203167d8931b6ce4f32ff0f7fc5021b79c2679d452a","abstract_canon_sha256":"74efde70ec08c4e77591a3d58de2688fac4f6d5ed84ddf0651e08a66ba834e62"},"schema_version":"1.0"},"canonical_sha256":"7558b0cd06aea6b0cbd44afdec4ffc792b9e2538cd77b31d27b1860d08ee86a5","source":{"kind":"arxiv","id":"1804.00222","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.00222","created_at":"2026-05-17T23:52:44Z"},{"alias_kind":"arxiv_version","alias_value":"1804.00222v3","created_at":"2026-05-17T23:52:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.00222","created_at":"2026-05-17T23:52:44Z"},{"alias_kind":"pith_short_12","alias_value":"OVMLBTIGV2TL","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"OVMLBTIGV2TLBS6U","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"OVMLBTIG","created_at":"2026-05-18T12:32:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:OVMLBTIGV2TLBS6UJL66YT74PE","target":"record","payload":{"canonical_record":{"source":{"id":"1804.00222","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-31T22:44:28Z","cross_cats_sorted":["cs.NE","stat.ML"],"title_canon_sha256":"c2333451cdd43feedfd80203167d8931b6ce4f32ff0f7fc5021b79c2679d452a","abstract_canon_sha256":"74efde70ec08c4e77591a3d58de2688fac4f6d5ed84ddf0651e08a66ba834e62"},"schema_version":"1.0"},"canonical_sha256":"7558b0cd06aea6b0cbd44afdec4ffc792b9e2538cd77b31d27b1860d08ee86a5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:44.161943Z","signature_b64":"6/D2ieOtmlN+AwmNGn3c67bQ4zRNDHxkSZXcQaZmYmLBaZU94HOVwLo38iig/dzpe4Oi/qEC98GQkdrLSUcnAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7558b0cd06aea6b0cbd44afdec4ffc792b9e2538cd77b31d27b1860d08ee86a5","last_reissued_at":"2026-05-17T23:52:44.161257Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:44.161257Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1804.00222","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":"2026-05-17T23:52:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rrl4g0BrKHg5hQ898NfFxNxayTnyLx+x2k1A+s8U/b+S3QrAYyM9/pDnmwF0HnWZ8atrW2I6ifvjf2kOsVAFAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T21:58:34.531587Z"},"content_sha256":"37f434cfa7bf46b7402434ca13ad12e4d7068e87f44e38537eb70db8921e3915","schema_version":"1.0","event_id":"sha256:37f434cfa7bf46b7402434ca13ad12e4d7068e87f44e38537eb70db8921e3915"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:OVMLBTIGV2TLBS6UJL66YT74PE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Meta-Learning Update Rules for Unsupervised Representation Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE","stat.ML"],"primary_cat":"cs.LG","authors_text":"Brian Cheung, Jascha Sohl-Dickstein, Luke Metz, Niru Maheswaranathan","submitted_at":"2018-03-31T22:44:28Z","abstract_excerpt":"A major goal of unsupervised learning is to discover data representations that are useful for subsequent tasks, without access to supervised labels during training. Typically, this involves minimizing a surrogate objective, such as the negative log likelihood of a generative model, with the hope that representations useful for subsequent tasks will arise as a side effect. In this work, we propose instead to directly target later desired tasks by meta-learning an unsupervised learning rule which leads to representations useful for those tasks. Specifically, we target semi-supervised classificat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.00222","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":"2026-05-17T23:52:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"w0EyhK1C8dpv092tWiIcdXY/KukUBnPclrF+FzA/bmiCmjo21GLz9tNEVKXuJbJB+/MGAkxp7KpgVm7QrMuFCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T21:58:34.532029Z"},"content_sha256":"199b1eccd1cb24cbb408f8c8dd5eada78b313b1ea527a5227b89d39710deddbc","schema_version":"1.0","event_id":"sha256:199b1eccd1cb24cbb408f8c8dd5eada78b313b1ea527a5227b89d39710deddbc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OVMLBTIGV2TLBS6UJL66YT74PE/bundle.json","state_url":"https://pith.science/pith/OVMLBTIGV2TLBS6UJL66YT74PE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OVMLBTIGV2TLBS6UJL66YT74PE/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":"2026-05-28T21:58:34Z","links":{"resolver":"https://pith.science/pith/OVMLBTIGV2TLBS6UJL66YT74PE","bundle":"https://pith.science/pith/OVMLBTIGV2TLBS6UJL66YT74PE/bundle.json","state":"https://pith.science/pith/OVMLBTIGV2TLBS6UJL66YT74PE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OVMLBTIGV2TLBS6UJL66YT74PE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:OVMLBTIGV2TLBS6UJL66YT74PE","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":"74efde70ec08c4e77591a3d58de2688fac4f6d5ed84ddf0651e08a66ba834e62","cross_cats_sorted":["cs.NE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-31T22:44:28Z","title_canon_sha256":"c2333451cdd43feedfd80203167d8931b6ce4f32ff0f7fc5021b79c2679d452a"},"schema_version":"1.0","source":{"id":"1804.00222","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.00222","created_at":"2026-05-17T23:52:44Z"},{"alias_kind":"arxiv_version","alias_value":"1804.00222v3","created_at":"2026-05-17T23:52:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.00222","created_at":"2026-05-17T23:52:44Z"},{"alias_kind":"pith_short_12","alias_value":"OVMLBTIGV2TL","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"OVMLBTIGV2TLBS6U","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"OVMLBTIG","created_at":"2026-05-18T12:32:43Z"}],"graph_snapshots":[{"event_id":"sha256:199b1eccd1cb24cbb408f8c8dd5eada78b313b1ea527a5227b89d39710deddbc","target":"graph","created_at":"2026-05-17T23:52:44Z","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":"A major goal of unsupervised learning is to discover data representations that are useful for subsequent tasks, without access to supervised labels during training. Typically, this involves minimizing a surrogate objective, such as the negative log likelihood of a generative model, with the hope that representations useful for subsequent tasks will arise as a side effect. In this work, we propose instead to directly target later desired tasks by meta-learning an unsupervised learning rule which leads to representations useful for those tasks. Specifically, we target semi-supervised classificat","authors_text":"Brian Cheung, Jascha Sohl-Dickstein, Luke Metz, Niru Maheswaranathan","cross_cats":["cs.NE","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-31T22:44:28Z","title":"Meta-Learning Update Rules for Unsupervised Representation Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.00222","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":"sha256:37f434cfa7bf46b7402434ca13ad12e4d7068e87f44e38537eb70db8921e3915","target":"record","created_at":"2026-05-17T23:52:44Z","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":"74efde70ec08c4e77591a3d58de2688fac4f6d5ed84ddf0651e08a66ba834e62","cross_cats_sorted":["cs.NE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-31T22:44:28Z","title_canon_sha256":"c2333451cdd43feedfd80203167d8931b6ce4f32ff0f7fc5021b79c2679d452a"},"schema_version":"1.0","source":{"id":"1804.00222","kind":"arxiv","version":3}},"canonical_sha256":"7558b0cd06aea6b0cbd44afdec4ffc792b9e2538cd77b31d27b1860d08ee86a5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7558b0cd06aea6b0cbd44afdec4ffc792b9e2538cd77b31d27b1860d08ee86a5","first_computed_at":"2026-05-17T23:52:44.161257Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:52:44.161257Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6/D2ieOtmlN+AwmNGn3c67bQ4zRNDHxkSZXcQaZmYmLBaZU94HOVwLo38iig/dzpe4Oi/qEC98GQkdrLSUcnAg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:52:44.161943Z","signed_message":"canonical_sha256_bytes"},"source_id":"1804.00222","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:37f434cfa7bf46b7402434ca13ad12e4d7068e87f44e38537eb70db8921e3915","sha256:199b1eccd1cb24cbb408f8c8dd5eada78b313b1ea527a5227b89d39710deddbc"],"state_sha256":"abd114a0407ce6ba7dbdbcd5bbf6190a871ea2acf9c7a1aff7a2373ddd7c440c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Qfx1atMQ+HOX/gWKm27pxKnu7c9TmSqDlkVzhOLsJflflOAE1QG9i5gM5wC13z0BV1J6LMImGrrjRFgha4EUCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T21:58:34.535840Z","bundle_sha256":"997880919a51b6787e532feaa516dd437358f871941b93cf0721cc4334f36789"}}