{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:6TDJXRRVG3KWYBW7SVU4KIFUSS","short_pith_number":"pith:6TDJXRRV","canonical_record":{"source":{"id":"1708.06040","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-08-21T00:44:32Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"bc06d0efbda8f1a7e61722ede08728bab35f9ff903a8f410c7445f4edcf97cb2","abstract_canon_sha256":"192b17d012c8183dd8495dbd7be94bfbe69538c6d08ad9f9539fc5bf9dc7dd4c"},"schema_version":"1.0"},"canonical_sha256":"f4c69bc63536d56c06df9569c520b494af8ce3040f12c307b6651e2e76b68f4b","source":{"kind":"arxiv","id":"1708.06040","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.06040","created_at":"2026-05-17T23:57:07Z"},{"alias_kind":"arxiv_version","alias_value":"1708.06040v5","created_at":"2026-05-17T23:57:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.06040","created_at":"2026-05-17T23:57:07Z"},{"alias_kind":"pith_short_12","alias_value":"6TDJXRRVG3KW","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_16","alias_value":"6TDJXRRVG3KWYBW7","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_8","alias_value":"6TDJXRRV","created_at":"2026-05-18T12:31:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:6TDJXRRVG3KWYBW7SVU4KIFUSS","target":"record","payload":{"canonical_record":{"source":{"id":"1708.06040","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-08-21T00:44:32Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"bc06d0efbda8f1a7e61722ede08728bab35f9ff903a8f410c7445f4edcf97cb2","abstract_canon_sha256":"192b17d012c8183dd8495dbd7be94bfbe69538c6d08ad9f9539fc5bf9dc7dd4c"},"schema_version":"1.0"},"canonical_sha256":"f4c69bc63536d56c06df9569c520b494af8ce3040f12c307b6651e2e76b68f4b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:57:07.808975Z","signature_b64":"6hDq7B15EVE6HuCGI+aVycf8RLMXi/LBb5C5TSocZGmRqlVKGAlaHVr/JWPAZEBsYsNnDGQrJdsG7GRaF3ZgDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f4c69bc63536d56c06df9569c520b494af8ce3040f12c307b6651e2e76b68f4b","last_reissued_at":"2026-05-17T23:57:07.808545Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:57:07.808545Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1708.06040","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":"2026-05-17T23:57:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KTfk2Gs7HMsx6hcx3LYc49Dfm8umBL75C84QJp6e1YdC12v0ui4o3hqOi7f6IeSNMpcmSfh/Cf10tDtxzvjeAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T04:41:06.887225Z"},"content_sha256":"413606cc9cf37b0ce7c98321f1b8e437a01ed99af7167bd5105e7d71c45eb992","schema_version":"1.0","event_id":"sha256:413606cc9cf37b0ce7c98321f1b8e437a01ed99af7167bd5105e7d71c45eb992"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:6TDJXRRVG3KWYBW7SVU4KIFUSS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Meta-Learning MCMC Proposals","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.AI","authors_text":"David A. Moore, Stuart J. Russell, Tongzhou Wang, Yi Wu","submitted_at":"2017-08-21T00:44:32Z","abstract_excerpt":"Effective implementations of sampling-based probabilistic inference often require manually constructed, model-specific proposals. Inspired by recent progresses in meta-learning for training learning agents that can generalize to unseen environments, we propose a meta-learning approach to building effective and generalizable MCMC proposals. We parametrize the proposal as a neural network to provide fast approximations to block Gibbs conditionals. The learned neural proposals generalize to occurrences of common structural motifs across different models, allowing for the construction of a library"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.06040","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":"2026-05-17T23:57:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"I86OMEdj+C+hcUQ2MeApV/1t5owro117Hu+eTQvaJb/NUxwpWx8/90/Hmsz1k/oxM+lHDsfraztJnQu8UTpIAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T04:41:06.887592Z"},"content_sha256":"cb869545d92bac171a541fb4b07404617709efce0736ef64897623662e90b3fc","schema_version":"1.0","event_id":"sha256:cb869545d92bac171a541fb4b07404617709efce0736ef64897623662e90b3fc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6TDJXRRVG3KWYBW7SVU4KIFUSS/bundle.json","state_url":"https://pith.science/pith/6TDJXRRVG3KWYBW7SVU4KIFUSS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6TDJXRRVG3KWYBW7SVU4KIFUSS/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":"2026-05-28T04:41:06Z","links":{"resolver":"https://pith.science/pith/6TDJXRRVG3KWYBW7SVU4KIFUSS","bundle":"https://pith.science/pith/6TDJXRRVG3KWYBW7SVU4KIFUSS/bundle.json","state":"https://pith.science/pith/6TDJXRRVG3KWYBW7SVU4KIFUSS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6TDJXRRVG3KWYBW7SVU4KIFUSS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:6TDJXRRVG3KWYBW7SVU4KIFUSS","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":"192b17d012c8183dd8495dbd7be94bfbe69538c6d08ad9f9539fc5bf9dc7dd4c","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-08-21T00:44:32Z","title_canon_sha256":"bc06d0efbda8f1a7e61722ede08728bab35f9ff903a8f410c7445f4edcf97cb2"},"schema_version":"1.0","source":{"id":"1708.06040","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.06040","created_at":"2026-05-17T23:57:07Z"},{"alias_kind":"arxiv_version","alias_value":"1708.06040v5","created_at":"2026-05-17T23:57:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.06040","created_at":"2026-05-17T23:57:07Z"},{"alias_kind":"pith_short_12","alias_value":"6TDJXRRVG3KW","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_16","alias_value":"6TDJXRRVG3KWYBW7","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_8","alias_value":"6TDJXRRV","created_at":"2026-05-18T12:31:03Z"}],"graph_snapshots":[{"event_id":"sha256:cb869545d92bac171a541fb4b07404617709efce0736ef64897623662e90b3fc","target":"graph","created_at":"2026-05-17T23:57:07Z","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":"Effective implementations of sampling-based probabilistic inference often require manually constructed, model-specific proposals. Inspired by recent progresses in meta-learning for training learning agents that can generalize to unseen environments, we propose a meta-learning approach to building effective and generalizable MCMC proposals. We parametrize the proposal as a neural network to provide fast approximations to block Gibbs conditionals. The learned neural proposals generalize to occurrences of common structural motifs across different models, allowing for the construction of a library","authors_text":"David A. Moore, Stuart J. Russell, Tongzhou Wang, Yi Wu","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-08-21T00:44:32Z","title":"Meta-Learning MCMC Proposals"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.06040","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":"sha256:413606cc9cf37b0ce7c98321f1b8e437a01ed99af7167bd5105e7d71c45eb992","target":"record","created_at":"2026-05-17T23:57:07Z","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":"192b17d012c8183dd8495dbd7be94bfbe69538c6d08ad9f9539fc5bf9dc7dd4c","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-08-21T00:44:32Z","title_canon_sha256":"bc06d0efbda8f1a7e61722ede08728bab35f9ff903a8f410c7445f4edcf97cb2"},"schema_version":"1.0","source":{"id":"1708.06040","kind":"arxiv","version":5}},"canonical_sha256":"f4c69bc63536d56c06df9569c520b494af8ce3040f12c307b6651e2e76b68f4b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f4c69bc63536d56c06df9569c520b494af8ce3040f12c307b6651e2e76b68f4b","first_computed_at":"2026-05-17T23:57:07.808545Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:57:07.808545Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6hDq7B15EVE6HuCGI+aVycf8RLMXi/LBb5C5TSocZGmRqlVKGAlaHVr/JWPAZEBsYsNnDGQrJdsG7GRaF3ZgDQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:57:07.808975Z","signed_message":"canonical_sha256_bytes"},"source_id":"1708.06040","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:413606cc9cf37b0ce7c98321f1b8e437a01ed99af7167bd5105e7d71c45eb992","sha256:cb869545d92bac171a541fb4b07404617709efce0736ef64897623662e90b3fc"],"state_sha256":"a304cdfe958da8ff4889acb87f16719fabae939fd52e5237481ea21491244fbd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"M24dyKRQiJCrOBiRwQIwyZSj2dYBTqTtcsx2ymawwg2MYslbNrp6l8wRHR9ZArqGdM15K0mGBtbLEuqTEwAwAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T04:41:06.889570Z","bundle_sha256":"e8daa4c9b1180a7c0f33200400c8d110d9da2b1e4d475c6636cf3db5eb5be795"}}