{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:TN6DIGA56DNDBCXMFS4IOVM74Q","short_pith_number":"pith:TN6DIGA5","canonical_record":{"source":{"id":"1810.10180","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-10-24T04:04:25Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"82e19201c0c669c553b6eb014886fbb63adf6a9190771749b57471b818cdaa26","abstract_canon_sha256":"eb2f162b485f8b7278cdaee7b9cdb07618e3a1835226c484becfb59acb38e838"},"schema_version":"1.0"},"canonical_sha256":"9b7c34181df0da308aec2cb887559fe438bbaf73a1790b4a67cf9bf28831109e","source":{"kind":"arxiv","id":"1810.10180","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.10180","created_at":"2026-05-17T23:43:50Z"},{"alias_kind":"arxiv_version","alias_value":"1810.10180v5","created_at":"2026-05-17T23:43:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.10180","created_at":"2026-05-17T23:43:50Z"},{"alias_kind":"pith_short_12","alias_value":"TN6DIGA56DND","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"TN6DIGA56DNDBCXM","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"TN6DIGA5","created_at":"2026-05-18T12:32:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:TN6DIGA56DNDBCXMFS4IOVM74Q","target":"record","payload":{"canonical_record":{"source":{"id":"1810.10180","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-10-24T04:04:25Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"82e19201c0c669c553b6eb014886fbb63adf6a9190771749b57471b818cdaa26","abstract_canon_sha256":"eb2f162b485f8b7278cdaee7b9cdb07618e3a1835226c484becfb59acb38e838"},"schema_version":"1.0"},"canonical_sha256":"9b7c34181df0da308aec2cb887559fe438bbaf73a1790b4a67cf9bf28831109e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:50.630557Z","signature_b64":"njh3AV20SRHb0dYNDT5pRWCTMhIqMS5HFAHKCV87tYEJ98Cb5t0H3PqnqrbPvL8kGoHR55uvZUlt39DOJRlHAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9b7c34181df0da308aec2cb887559fe438bbaf73a1790b4a67cf9bf28831109e","last_reissued_at":"2026-05-17T23:43:50.630076Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:50.630076Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1810.10180","source_version":5,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:43:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YPAtSgruFcsdAJq20GK8EPbQqC8eZS5cUlwtZrwq692ybw4q5eMeF3QerM5HvfV/8Yjjk2Gukw/hqn6GI5xwBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T17:20:39.720289Z"},"content_sha256":"6b791b440bf86937842603fde0651ee08d9c594d68183a13a639b0276b5f641d","schema_version":"1.0","event_id":"sha256:6b791b440bf86937842603fde0651ee08d9c594d68183a13a639b0276b5f641d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:TN6DIGA56DNDBCXMFS4IOVM74Q","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Understanding and correcting pathologies in the training of learned optimizers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.NE","authors_text":"C. Daniel Freeman, Jascha Sohl-Dickstein, Jeremy Nixon, Luke Metz, Niru Maheswaranathan","submitted_at":"2018-10-24T04:04:25Z","abstract_excerpt":"Deep learning has shown that learned functions can dramatically outperform hand-designed functions on perceptual tasks. Analogously, this suggests that learned optimizers may similarly outperform current hand-designed optimizers, especially for specific problems. However, learned optimizers are notoriously difficult to train and have yet to demonstrate wall-clock speedups over hand-designed optimizers, and thus are rarely used in practice. Typically, learned optimizers are trained by truncated backpropagation through an unrolled optimization process resulting in gradients that are either stron"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.10180","kind":"arxiv","version":5},"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":"2026-05-17T23:43:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4YaKm11TyiOdgFsOeBz09EGCPhmQX9iMjrVVKyXk/9U7prSdEfHp3iXZNidujSu16hm/Ld0DVmTjn7AcpZH+Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T17:20:39.721077Z"},"content_sha256":"e93deedfd1d72ac1dc0122fe4ca368a166e4cdb452b0f831a35a9f9ebba071f9","schema_version":"1.0","event_id":"sha256:e93deedfd1d72ac1dc0122fe4ca368a166e4cdb452b0f831a35a9f9ebba071f9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TN6DIGA56DNDBCXMFS4IOVM74Q/bundle.json","state_url":"https://pith.science/pith/TN6DIGA56DNDBCXMFS4IOVM74Q/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TN6DIGA56DNDBCXMFS4IOVM74Q/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":"2026-05-28T17:20:39Z","links":{"resolver":"https://pith.science/pith/TN6DIGA56DNDBCXMFS4IOVM74Q","bundle":"https://pith.science/pith/TN6DIGA56DNDBCXMFS4IOVM74Q/bundle.json","state":"https://pith.science/pith/TN6DIGA56DNDBCXMFS4IOVM74Q/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TN6DIGA56DNDBCXMFS4IOVM74Q/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:TN6DIGA56DNDBCXMFS4IOVM74Q","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":"eb2f162b485f8b7278cdaee7b9cdb07618e3a1835226c484becfb59acb38e838","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-10-24T04:04:25Z","title_canon_sha256":"82e19201c0c669c553b6eb014886fbb63adf6a9190771749b57471b818cdaa26"},"schema_version":"1.0","source":{"id":"1810.10180","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.10180","created_at":"2026-05-17T23:43:50Z"},{"alias_kind":"arxiv_version","alias_value":"1810.10180v5","created_at":"2026-05-17T23:43:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.10180","created_at":"2026-05-17T23:43:50Z"},{"alias_kind":"pith_short_12","alias_value":"TN6DIGA56DND","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"TN6DIGA56DNDBCXM","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"TN6DIGA5","created_at":"2026-05-18T12:32:53Z"}],"graph_snapshots":[{"event_id":"sha256:e93deedfd1d72ac1dc0122fe4ca368a166e4cdb452b0f831a35a9f9ebba071f9","target":"graph","created_at":"2026-05-17T23:43:50Z","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":"Deep learning has shown that learned functions can dramatically outperform hand-designed functions on perceptual tasks. Analogously, this suggests that learned optimizers may similarly outperform current hand-designed optimizers, especially for specific problems. However, learned optimizers are notoriously difficult to train and have yet to demonstrate wall-clock speedups over hand-designed optimizers, and thus are rarely used in practice. Typically, learned optimizers are trained by truncated backpropagation through an unrolled optimization process resulting in gradients that are either stron","authors_text":"C. Daniel Freeman, Jascha Sohl-Dickstein, Jeremy Nixon, Luke Metz, Niru Maheswaranathan","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-10-24T04:04:25Z","title":"Understanding and correcting pathologies in the training of learned optimizers"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.10180","kind":"arxiv","version":5},"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":"sha256:6b791b440bf86937842603fde0651ee08d9c594d68183a13a639b0276b5f641d","target":"record","created_at":"2026-05-17T23:43:50Z","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":"eb2f162b485f8b7278cdaee7b9cdb07618e3a1835226c484becfb59acb38e838","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-10-24T04:04:25Z","title_canon_sha256":"82e19201c0c669c553b6eb014886fbb63adf6a9190771749b57471b818cdaa26"},"schema_version":"1.0","source":{"id":"1810.10180","kind":"arxiv","version":5}},"canonical_sha256":"9b7c34181df0da308aec2cb887559fe438bbaf73a1790b4a67cf9bf28831109e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9b7c34181df0da308aec2cb887559fe438bbaf73a1790b4a67cf9bf28831109e","first_computed_at":"2026-05-17T23:43:50.630076Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:43:50.630076Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"njh3AV20SRHb0dYNDT5pRWCTMhIqMS5HFAHKCV87tYEJ98Cb5t0H3PqnqrbPvL8kGoHR55uvZUlt39DOJRlHAg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:43:50.630557Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.10180","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6b791b440bf86937842603fde0651ee08d9c594d68183a13a639b0276b5f641d","sha256:e93deedfd1d72ac1dc0122fe4ca368a166e4cdb452b0f831a35a9f9ebba071f9"],"state_sha256":"958a464857503cddcf232f1b29395e9b37bf69168ae338793e79430ba79a4cde"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+oXWyX2kFcTkZ+nxwC6WajCBL92zIYHRLywcd78eSdwjhBrtn8JbfNJfHv7/HVK+mxxXGeIKKbqRYdiCjlLJCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T17:20:39.724036Z","bundle_sha256":"ad914f0edb1720383d70d10140afee941af34114ce443c44ddee68c5e2c1a8cc"}}