{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:GVDDQQXE2VJ2O6NYAPZT4OH5HO","short_pith_number":"pith:GVDDQQXE","canonical_record":{"source":{"id":"1709.00069","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-31T20:33:06Z","cross_cats_sorted":[],"title_canon_sha256":"af0540ff665a9943cdb85429845984c1d62fdde2469da36554ffcd185b861bcf","abstract_canon_sha256":"ed74c85ba97db444d65fec1e7341a439443f24386c7d2a1908c469af65cc6328"},"schema_version":"1.0"},"canonical_sha256":"35463842e4d553a779b803f33e38fd3ba2b9f4f21318094c9f9880957e6192b1","source":{"kind":"arxiv","id":"1709.00069","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.00069","created_at":"2026-05-18T00:36:13Z"},{"alias_kind":"arxiv_version","alias_value":"1709.00069v1","created_at":"2026-05-18T00:36:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.00069","created_at":"2026-05-18T00:36:13Z"},{"alias_kind":"pith_short_12","alias_value":"GVDDQQXE2VJ2","created_at":"2026-05-18T12:31:18Z"},{"alias_kind":"pith_short_16","alias_value":"GVDDQQXE2VJ2O6NY","created_at":"2026-05-18T12:31:18Z"},{"alias_kind":"pith_short_8","alias_value":"GVDDQQXE","created_at":"2026-05-18T12:31:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:GVDDQQXE2VJ2O6NYAPZT4OH5HO","target":"record","payload":{"canonical_record":{"source":{"id":"1709.00069","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-31T20:33:06Z","cross_cats_sorted":[],"title_canon_sha256":"af0540ff665a9943cdb85429845984c1d62fdde2469da36554ffcd185b861bcf","abstract_canon_sha256":"ed74c85ba97db444d65fec1e7341a439443f24386c7d2a1908c469af65cc6328"},"schema_version":"1.0"},"canonical_sha256":"35463842e4d553a779b803f33e38fd3ba2b9f4f21318094c9f9880957e6192b1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:36:13.531847Z","signature_b64":"Pah5uX6fe+tMJaoyYfSzwzDZH6/YC8sk23VjNJUz7y5uhMvXIUkvUI+psn9wJrC2ugnqI3iiPuCter5984q2Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"35463842e4d553a779b803f33e38fd3ba2b9f4f21318094c9f9880957e6192b1","last_reissued_at":"2026-05-18T00:36:13.531196Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:36:13.531196Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1709.00069","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":"2026-05-18T00:36:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nMjE77GRO6Vkxdl9fIo8dYbLlBiuTUsj8hULszaViZxzmf6Pef2eDvDPrLo3raGAz1u4M5C9lzQFXTLHTQV6DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T05:53:12.200646Z"},"content_sha256":"e496bcabf8c65a3efece7900ad32886b2a96e9304d88e298e18d6e785f9b494d","schema_version":"1.0","event_id":"sha256:e496bcabf8c65a3efece7900ad32886b2a96e9304d88e298e18d6e785f9b494d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:GVDDQQXE2VJ2O6NYAPZT4OH5HO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Inference Models for Computer Vision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Varun Jampani","submitted_at":"2017-08-31T20:33:06Z","abstract_excerpt":"Computer vision can be understood as the ability to perform inference on image data. Breakthroughs in computer vision technology are often marked by advances in inference techniques. This thesis proposes novel inference schemes and demonstrates applications in computer vision. We propose inference techniques for both generative and discriminative vision models. The use of generative models in vision is often hampered by the difficulty of posterior inference. We propose techniques for improving inference in MCMC sampling and message-passing inference. Our inference strategy is to learn separate"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.00069","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":"2026-05-18T00:36:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mbBaydIAsnGcm1jXZQXJ3QxknRW0I4XpeXVfC5Xb+uhHLFwT9RN/JpGivpIb4xwpMWJPBDbZuGxxA8S2N0u0Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T05:53:12.201382Z"},"content_sha256":"0d2c362e8b3a30b51062dd62fd81a73a33ebcb53201ae47cdd838eafce413a93","schema_version":"1.0","event_id":"sha256:0d2c362e8b3a30b51062dd62fd81a73a33ebcb53201ae47cdd838eafce413a93"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GVDDQQXE2VJ2O6NYAPZT4OH5HO/bundle.json","state_url":"https://pith.science/pith/GVDDQQXE2VJ2O6NYAPZT4OH5HO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GVDDQQXE2VJ2O6NYAPZT4OH5HO/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":"2026-06-06T05:53:12Z","links":{"resolver":"https://pith.science/pith/GVDDQQXE2VJ2O6NYAPZT4OH5HO","bundle":"https://pith.science/pith/GVDDQQXE2VJ2O6NYAPZT4OH5HO/bundle.json","state":"https://pith.science/pith/GVDDQQXE2VJ2O6NYAPZT4OH5HO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GVDDQQXE2VJ2O6NYAPZT4OH5HO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:GVDDQQXE2VJ2O6NYAPZT4OH5HO","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":"ed74c85ba97db444d65fec1e7341a439443f24386c7d2a1908c469af65cc6328","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-31T20:33:06Z","title_canon_sha256":"af0540ff665a9943cdb85429845984c1d62fdde2469da36554ffcd185b861bcf"},"schema_version":"1.0","source":{"id":"1709.00069","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.00069","created_at":"2026-05-18T00:36:13Z"},{"alias_kind":"arxiv_version","alias_value":"1709.00069v1","created_at":"2026-05-18T00:36:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.00069","created_at":"2026-05-18T00:36:13Z"},{"alias_kind":"pith_short_12","alias_value":"GVDDQQXE2VJ2","created_at":"2026-05-18T12:31:18Z"},{"alias_kind":"pith_short_16","alias_value":"GVDDQQXE2VJ2O6NY","created_at":"2026-05-18T12:31:18Z"},{"alias_kind":"pith_short_8","alias_value":"GVDDQQXE","created_at":"2026-05-18T12:31:18Z"}],"graph_snapshots":[{"event_id":"sha256:0d2c362e8b3a30b51062dd62fd81a73a33ebcb53201ae47cdd838eafce413a93","target":"graph","created_at":"2026-05-18T00:36:13Z","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":"Computer vision can be understood as the ability to perform inference on image data. Breakthroughs in computer vision technology are often marked by advances in inference techniques. This thesis proposes novel inference schemes and demonstrates applications in computer vision. We propose inference techniques for both generative and discriminative vision models. The use of generative models in vision is often hampered by the difficulty of posterior inference. We propose techniques for improving inference in MCMC sampling and message-passing inference. Our inference strategy is to learn separate","authors_text":"Varun Jampani","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-31T20:33:06Z","title":"Learning Inference Models for Computer Vision"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.00069","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:e496bcabf8c65a3efece7900ad32886b2a96e9304d88e298e18d6e785f9b494d","target":"record","created_at":"2026-05-18T00:36:13Z","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":"ed74c85ba97db444d65fec1e7341a439443f24386c7d2a1908c469af65cc6328","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-31T20:33:06Z","title_canon_sha256":"af0540ff665a9943cdb85429845984c1d62fdde2469da36554ffcd185b861bcf"},"schema_version":"1.0","source":{"id":"1709.00069","kind":"arxiv","version":1}},"canonical_sha256":"35463842e4d553a779b803f33e38fd3ba2b9f4f21318094c9f9880957e6192b1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"35463842e4d553a779b803f33e38fd3ba2b9f4f21318094c9f9880957e6192b1","first_computed_at":"2026-05-18T00:36:13.531196Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:36:13.531196Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Pah5uX6fe+tMJaoyYfSzwzDZH6/YC8sk23VjNJUz7y5uhMvXIUkvUI+psn9wJrC2ugnqI3iiPuCter5984q2Bg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:36:13.531847Z","signed_message":"canonical_sha256_bytes"},"source_id":"1709.00069","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e496bcabf8c65a3efece7900ad32886b2a96e9304d88e298e18d6e785f9b494d","sha256:0d2c362e8b3a30b51062dd62fd81a73a33ebcb53201ae47cdd838eafce413a93"],"state_sha256":"ae28c90f5c9690e710d569cb6711f0d55124d070d57fb395c278f9218f99886a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wJHVr+i74787yNsoZL/DySdQAoA1HLhNY1kI3mW10lQIFjGV8AmxIp/PyzKoykbDh4MGUXy61yQAfLn8IsFHDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T05:53:12.209004Z","bundle_sha256":"35dfd67098d729fe42e734f8258515df9f798df82388df3f3ec1852251c56b2e"}}