{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:F74KQGQUKPF653NOEHGGORGLXL","short_pith_number":"pith:F74KQGQU","canonical_record":{"source":{"id":"1703.04813","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-03-14T23:05:54Z","cross_cats_sorted":["cs.NE","stat.ML"],"title_canon_sha256":"996bc100c4eb0387e7da871d9b8203723ba53ec61184cfc551b3609d96e65bc4","abstract_canon_sha256":"77515ddeec72e21f870e58f0cf76edf552a8160d1ddb2c3c1bede95f0fe76050"},"schema_version":"1.0"},"canonical_sha256":"2ff8a81a1453cbeeedae21cc6744cbbaefb0f63f72085a4045168730d2aabbd3","source":{"kind":"arxiv","id":"1703.04813","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.04813","created_at":"2026-05-18T00:35:46Z"},{"alias_kind":"arxiv_version","alias_value":"1703.04813v4","created_at":"2026-05-18T00:35:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.04813","created_at":"2026-05-18T00:35:46Z"},{"alias_kind":"pith_short_12","alias_value":"F74KQGQUKPF6","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"F74KQGQUKPF653NO","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"F74KQGQU","created_at":"2026-05-18T12:31:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:F74KQGQUKPF653NOEHGGORGLXL","target":"record","payload":{"canonical_record":{"source":{"id":"1703.04813","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-03-14T23:05:54Z","cross_cats_sorted":["cs.NE","stat.ML"],"title_canon_sha256":"996bc100c4eb0387e7da871d9b8203723ba53ec61184cfc551b3609d96e65bc4","abstract_canon_sha256":"77515ddeec72e21f870e58f0cf76edf552a8160d1ddb2c3c1bede95f0fe76050"},"schema_version":"1.0"},"canonical_sha256":"2ff8a81a1453cbeeedae21cc6744cbbaefb0f63f72085a4045168730d2aabbd3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:35:46.449383Z","signature_b64":"eTZLPxxP8q6sQE69ClTcppU+DnyS0sPMlPmGQbnByRPWrugmMQSjNVgCX3QuYAnloMFBbB1jxPy/cLJZBpQODg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2ff8a81a1453cbeeedae21cc6744cbbaefb0f63f72085a4045168730d2aabbd3","last_reissued_at":"2026-05-18T00:35:46.448846Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:35:46.448846Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1703.04813","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:35:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"V6fMpeJkbarkKHsadfAPYhs/HWxfHjaVEuw3MV+Ft1w6GAU3F9y/Lcw7zFdHTz8Kk122RjVyAVt3YSONP+vKAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T16:43:23.422932Z"},"content_sha256":"c6cccba102502b57446d8246a7f87a2b03fc4f908be429ffcfa42a808868692b","schema_version":"1.0","event_id":"sha256:c6cccba102502b57446d8246a7f87a2b03fc4f908be429ffcfa42a808868692b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:F74KQGQUKPF653NOEHGGORGLXL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learned Optimizers that Scale and Generalize","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE","stat.ML"],"primary_cat":"cs.LG","authors_text":"Jascha Sohl-Dickstein, Matthew W. Hoffman, Misha Denil, Nando de Freitas, Niru Maheswaranathan, Olga Wichrowska, Sergio Gomez Colmenarejo","submitted_at":"2017-03-14T23:05:54Z","abstract_excerpt":"Learning to learn has emerged as an important direction for achieving artificial intelligence. Two of the primary barriers to its adoption are an inability to scale to larger problems and a limited ability to generalize to new tasks. We introduce a learned gradient descent optimizer that generalizes well to new tasks, and which has significantly reduced memory and computation overhead. We achieve this by introducing a novel hierarchical RNN architecture, with minimal per-parameter overhead, augmented with additional architectural features that mirror the known structure of optimization tasks. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.04813","kind":"arxiv","version":4},"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":"2026-05-18T00:35:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lgq20FjX21SAEB/sNvaD/I/q/ALMPWKz92OrP8bBycdpaTHYzPadmpwedUT7qoeoSnG9WZvq/rQkUUCc6QjhAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T16:43:23.423605Z"},"content_sha256":"67834a3e731ea051a8e80d967748678d4b9c38ca7a7a8c9e150f28fdeebc86c9","schema_version":"1.0","event_id":"sha256:67834a3e731ea051a8e80d967748678d4b9c38ca7a7a8c9e150f28fdeebc86c9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/F74KQGQUKPF653NOEHGGORGLXL/bundle.json","state_url":"https://pith.science/pith/F74KQGQUKPF653NOEHGGORGLXL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/F74KQGQUKPF653NOEHGGORGLXL/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":"2026-05-25T16:43:23Z","links":{"resolver":"https://pith.science/pith/F74KQGQUKPF653NOEHGGORGLXL","bundle":"https://pith.science/pith/F74KQGQUKPF653NOEHGGORGLXL/bundle.json","state":"https://pith.science/pith/F74KQGQUKPF653NOEHGGORGLXL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/F74KQGQUKPF653NOEHGGORGLXL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:F74KQGQUKPF653NOEHGGORGLXL","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":"77515ddeec72e21f870e58f0cf76edf552a8160d1ddb2c3c1bede95f0fe76050","cross_cats_sorted":["cs.NE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-03-14T23:05:54Z","title_canon_sha256":"996bc100c4eb0387e7da871d9b8203723ba53ec61184cfc551b3609d96e65bc4"},"schema_version":"1.0","source":{"id":"1703.04813","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.04813","created_at":"2026-05-18T00:35:46Z"},{"alias_kind":"arxiv_version","alias_value":"1703.04813v4","created_at":"2026-05-18T00:35:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.04813","created_at":"2026-05-18T00:35:46Z"},{"alias_kind":"pith_short_12","alias_value":"F74KQGQUKPF6","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"F74KQGQUKPF653NO","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"F74KQGQU","created_at":"2026-05-18T12:31:15Z"}],"graph_snapshots":[{"event_id":"sha256:67834a3e731ea051a8e80d967748678d4b9c38ca7a7a8c9e150f28fdeebc86c9","target":"graph","created_at":"2026-05-18T00:35:46Z","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":"Learning to learn has emerged as an important direction for achieving artificial intelligence. Two of the primary barriers to its adoption are an inability to scale to larger problems and a limited ability to generalize to new tasks. We introduce a learned gradient descent optimizer that generalizes well to new tasks, and which has significantly reduced memory and computation overhead. We achieve this by introducing a novel hierarchical RNN architecture, with minimal per-parameter overhead, augmented with additional architectural features that mirror the known structure of optimization tasks. ","authors_text":"Jascha Sohl-Dickstein, Matthew W. Hoffman, Misha Denil, Nando de Freitas, Niru Maheswaranathan, Olga Wichrowska, Sergio Gomez Colmenarejo","cross_cats":["cs.NE","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-03-14T23:05:54Z","title":"Learned Optimizers that Scale and Generalize"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.04813","kind":"arxiv","version":4},"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:c6cccba102502b57446d8246a7f87a2b03fc4f908be429ffcfa42a808868692b","target":"record","created_at":"2026-05-18T00:35:46Z","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":"77515ddeec72e21f870e58f0cf76edf552a8160d1ddb2c3c1bede95f0fe76050","cross_cats_sorted":["cs.NE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-03-14T23:05:54Z","title_canon_sha256":"996bc100c4eb0387e7da871d9b8203723ba53ec61184cfc551b3609d96e65bc4"},"schema_version":"1.0","source":{"id":"1703.04813","kind":"arxiv","version":4}},"canonical_sha256":"2ff8a81a1453cbeeedae21cc6744cbbaefb0f63f72085a4045168730d2aabbd3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2ff8a81a1453cbeeedae21cc6744cbbaefb0f63f72085a4045168730d2aabbd3","first_computed_at":"2026-05-18T00:35:46.448846Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:35:46.448846Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"eTZLPxxP8q6sQE69ClTcppU+DnyS0sPMlPmGQbnByRPWrugmMQSjNVgCX3QuYAnloMFBbB1jxPy/cLJZBpQODg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:35:46.449383Z","signed_message":"canonical_sha256_bytes"},"source_id":"1703.04813","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c6cccba102502b57446d8246a7f87a2b03fc4f908be429ffcfa42a808868692b","sha256:67834a3e731ea051a8e80d967748678d4b9c38ca7a7a8c9e150f28fdeebc86c9"],"state_sha256":"86401c40502341b15ffdf0180506591b4421ac5530f94ef859ffd12c14b1f1f8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9oHXuixraswZNtzwBRfZfzsTi0MCnnjLlzp6BOAGmo7zx23/6TI8mhFfrYkr3vGhwWxpfK3z6bEd10kuaFrPCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T16:43:23.427416Z","bundle_sha256":"0255a4bfcbc39279879abfe4bc04a3e160f6d761dbb8b1aa422b80dbfeccd27e"}}