{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:YU2KPZQIETNAMJ4OFS6QFM72QL","short_pith_number":"pith:YU2KPZQI","canonical_record":{"source":{"id":"1706.04008","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-06-13T11:24:41Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"3a51e9a4785d81cdef3457af3531c7ff0ba207fabb0d169e5b61bc9c543b4c20","abstract_canon_sha256":"f75bf733bb1e203e75b8446f1a64f713c8eba1da83498df00387e52546339ee6"},"schema_version":"1.0"},"canonical_sha256":"c534a7e60824da06278e2cbd02b3fa82fd39976654402590e997f8c6e84adaf0","source":{"kind":"arxiv","id":"1706.04008","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.04008","created_at":"2026-05-18T00:42:24Z"},{"alias_kind":"arxiv_version","alias_value":"1706.04008v1","created_at":"2026-05-18T00:42:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.04008","created_at":"2026-05-18T00:42:24Z"},{"alias_kind":"pith_short_12","alias_value":"YU2KPZQIETNA","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_16","alias_value":"YU2KPZQIETNAMJ4O","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_8","alias_value":"YU2KPZQI","created_at":"2026-05-18T12:31:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:YU2KPZQIETNAMJ4OFS6QFM72QL","target":"record","payload":{"canonical_record":{"source":{"id":"1706.04008","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-06-13T11:24:41Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"3a51e9a4785d81cdef3457af3531c7ff0ba207fabb0d169e5b61bc9c543b4c20","abstract_canon_sha256":"f75bf733bb1e203e75b8446f1a64f713c8eba1da83498df00387e52546339ee6"},"schema_version":"1.0"},"canonical_sha256":"c534a7e60824da06278e2cbd02b3fa82fd39976654402590e997f8c6e84adaf0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:42:24.751094Z","signature_b64":"xSbRjpSWwIdU/1bU+C197HojnUSsvdWyMb49hIQzRO3fM6HgY+abbrLkxHJc9qy+kXVcjeUEu62vvBW/EI36Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c534a7e60824da06278e2cbd02b3fa82fd39976654402590e997f8c6e84adaf0","last_reissued_at":"2026-05-18T00:42:24.750590Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:42:24.750590Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.04008","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":"2026-05-18T00:42:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CKk2EfHmx2s7jg9v2R8li4jk1wGI6VfTG8IFpHWGsF8KGO/ZExIasSbA/rHCgpU5lEVhJvK622t4L5+rsMk8BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T22:33:47.671390Z"},"content_sha256":"5155f640e9690157c97261c0d02662ffa7e4df837a0a7feb930fe867df974cd4","schema_version":"1.0","event_id":"sha256:5155f640e9690157c97261c0d02662ffa7e4df837a0a7feb930fe867df974cd4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:YU2KPZQIETNAMJ4OFS6QFM72QL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Recurrent Inference Machines for Solving Inverse Problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.NE","authors_text":"Max Welling, Patrick Putzky","submitted_at":"2017-06-13T11:24:41Z","abstract_excerpt":"Much of the recent research on solving iterative inference problems focuses on moving away from hand-chosen inference algorithms and towards learned inference. In the latter, the inference process is unrolled in time and interpreted as a recurrent neural network (RNN) which allows for joint learning of model and inference parameters with back-propagation through time. In this framework, the RNN architecture is directly derived from a hand-chosen inference algorithm, effectively limiting its capabilities. We propose a learning framework, called Recurrent Inference Machines (RIM), in which we tu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.04008","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":""},"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":"2026-05-18T00:42:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uvrkB0etfPi48VO1yC6WRNeg5CPayWaeKVCf5gRudX3RR/8zvWyS7OW13nnTAJpx8qm/E9SNU+qlrOglXL9yAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T22:33:47.671739Z"},"content_sha256":"73df93b324d6d992e24b159d152d9a956e94c5027dff4483e8bce11caf93e6e3","schema_version":"1.0","event_id":"sha256:73df93b324d6d992e24b159d152d9a956e94c5027dff4483e8bce11caf93e6e3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YU2KPZQIETNAMJ4OFS6QFM72QL/bundle.json","state_url":"https://pith.science/pith/YU2KPZQIETNAMJ4OFS6QFM72QL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YU2KPZQIETNAMJ4OFS6QFM72QL/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":"2026-06-08T22:33:47Z","links":{"resolver":"https://pith.science/pith/YU2KPZQIETNAMJ4OFS6QFM72QL","bundle":"https://pith.science/pith/YU2KPZQIETNAMJ4OFS6QFM72QL/bundle.json","state":"https://pith.science/pith/YU2KPZQIETNAMJ4OFS6QFM72QL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YU2KPZQIETNAMJ4OFS6QFM72QL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:YU2KPZQIETNAMJ4OFS6QFM72QL","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":"f75bf733bb1e203e75b8446f1a64f713c8eba1da83498df00387e52546339ee6","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-06-13T11:24:41Z","title_canon_sha256":"3a51e9a4785d81cdef3457af3531c7ff0ba207fabb0d169e5b61bc9c543b4c20"},"schema_version":"1.0","source":{"id":"1706.04008","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.04008","created_at":"2026-05-18T00:42:24Z"},{"alias_kind":"arxiv_version","alias_value":"1706.04008v1","created_at":"2026-05-18T00:42:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.04008","created_at":"2026-05-18T00:42:24Z"},{"alias_kind":"pith_short_12","alias_value":"YU2KPZQIETNA","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_16","alias_value":"YU2KPZQIETNAMJ4O","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_8","alias_value":"YU2KPZQI","created_at":"2026-05-18T12:31:56Z"}],"graph_snapshots":[{"event_id":"sha256:73df93b324d6d992e24b159d152d9a956e94c5027dff4483e8bce11caf93e6e3","target":"graph","created_at":"2026-05-18T00:42:24Z","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":"Much of the recent research on solving iterative inference problems focuses on moving away from hand-chosen inference algorithms and towards learned inference. In the latter, the inference process is unrolled in time and interpreted as a recurrent neural network (RNN) which allows for joint learning of model and inference parameters with back-propagation through time. In this framework, the RNN architecture is directly derived from a hand-chosen inference algorithm, effectively limiting its capabilities. We propose a learning framework, called Recurrent Inference Machines (RIM), in which we tu","authors_text":"Max Welling, Patrick Putzky","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-06-13T11:24:41Z","title":"Recurrent Inference Machines for Solving Inverse Problems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.04008","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":"sha256:5155f640e9690157c97261c0d02662ffa7e4df837a0a7feb930fe867df974cd4","target":"record","created_at":"2026-05-18T00:42:24Z","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":"f75bf733bb1e203e75b8446f1a64f713c8eba1da83498df00387e52546339ee6","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-06-13T11:24:41Z","title_canon_sha256":"3a51e9a4785d81cdef3457af3531c7ff0ba207fabb0d169e5b61bc9c543b4c20"},"schema_version":"1.0","source":{"id":"1706.04008","kind":"arxiv","version":1}},"canonical_sha256":"c534a7e60824da06278e2cbd02b3fa82fd39976654402590e997f8c6e84adaf0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c534a7e60824da06278e2cbd02b3fa82fd39976654402590e997f8c6e84adaf0","first_computed_at":"2026-05-18T00:42:24.750590Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:42:24.750590Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xSbRjpSWwIdU/1bU+C197HojnUSsvdWyMb49hIQzRO3fM6HgY+abbrLkxHJc9qy+kXVcjeUEu62vvBW/EI36Aw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:42:24.751094Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.04008","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5155f640e9690157c97261c0d02662ffa7e4df837a0a7feb930fe867df974cd4","sha256:73df93b324d6d992e24b159d152d9a956e94c5027dff4483e8bce11caf93e6e3"],"state_sha256":"b630f75eff3789f635a4ce0c568f4cefc665d46abf672d0c6d4207fb7d75dd78"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7IfQjc0XjUuqjUlkHVWc9lqkyWCY9ZfM1N0HxdCe3MjGpphnJDijOeBH0ElD8OCyhMGMGuWKdJUatRi1MMefAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T22:33:47.673652Z","bundle_sha256":"a1f9ddc1443d83061968f46ba398334252238143442d9d9bb114b785c1afa1ec"}}