{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:PRWI7ZW4X3ID7OY7UKYVWLUCUY","short_pith_number":"pith:PRWI7ZW4","canonical_record":{"source":{"id":"2105.05489","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-05-12T07:51:47Z","cross_cats_sorted":["cs.CV","cs.LG","stat.CO"],"title_canon_sha256":"8be110c4ddf2e0413c00a57a36d67895f66aa1293bfd1a0218c4d4c3d8c9b56a","abstract_canon_sha256":"2d4ce280ba972c605da8c791432425ca9bfab3a6a3aa771e0ccd2c174b393d35"},"schema_version":"1.0"},"canonical_sha256":"7c6c8fe6dcbed03fbb1fa2b15b2e82a611b73f710a534920bcd371028a2bd58f","source":{"kind":"arxiv","id":"2105.05489","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2105.05489","created_at":"2026-07-05T02:39:51Z"},{"alias_kind":"arxiv_version","alias_value":"2105.05489v1","created_at":"2026-07-05T02:39:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.05489","created_at":"2026-07-05T02:39:51Z"},{"alias_kind":"pith_short_12","alias_value":"PRWI7ZW4X3ID","created_at":"2026-07-05T02:39:51Z"},{"alias_kind":"pith_short_16","alias_value":"PRWI7ZW4X3ID7OY7","created_at":"2026-07-05T02:39:51Z"},{"alias_kind":"pith_short_8","alias_value":"PRWI7ZW4","created_at":"2026-07-05T02:39:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:PRWI7ZW4X3ID7OY7UKYVWLUCUY","target":"record","payload":{"canonical_record":{"source":{"id":"2105.05489","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-05-12T07:51:47Z","cross_cats_sorted":["cs.CV","cs.LG","stat.CO"],"title_canon_sha256":"8be110c4ddf2e0413c00a57a36d67895f66aa1293bfd1a0218c4d4c3d8c9b56a","abstract_canon_sha256":"2d4ce280ba972c605da8c791432425ca9bfab3a6a3aa771e0ccd2c174b393d35"},"schema_version":"1.0"},"canonical_sha256":"7c6c8fe6dcbed03fbb1fa2b15b2e82a611b73f710a534920bcd371028a2bd58f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:39:51.475983Z","signature_b64":"KwQMw3GPP7BiwhAiMc7WBIjXFsSweAvA0ltVW0VcT3w4i6LObhnmCXrgyQbE+yNevU2SfBpihII5rJcFxR+jCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7c6c8fe6dcbed03fbb1fa2b15b2e82a611b73f710a534920bcd371028a2bd58f","last_reissued_at":"2026-07-05T02:39:51.475548Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:39:51.475548Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2105.05489","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":"2026-07-05T02:39:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EPCmIGSL8lp+7vggb4h5gJVmDa96D/EQgAt7WqT9GLUV4GiW3Z0adUGzMxiYYtbuNhMXDG6URUSL2Hn1FltMAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T20:24:23.576324Z"},"content_sha256":"575da6084c8d026c24c119b836699c9ee16d6260d0803911b0881fabd18efe32","schema_version":"1.0","event_id":"sha256:575da6084c8d026c24c119b836699c9ee16d6260d0803911b0881fabd18efe32"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:PRWI7ZW4X3ID7OY7UKYVWLUCUY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG","stat.CO"],"primary_cat":"stat.ML","authors_text":"Pengchuan Zhang, Shumao Zhang, Thomas Y. Hou","submitted_at":"2021-05-12T07:51:47Z","abstract_excerpt":"We propose a Multiscale Invertible Generative Network (MsIGN) and associated training algorithm that leverages multiscale structure to solve high-dimensional Bayesian inference. To address the curse of dimensionality, MsIGN exploits the low-dimensional nature of the posterior, and generates samples from coarse to fine scale (low to high dimension) by iteratively upsampling and refining samples. MsIGN is trained in a multi-stage manner to minimize the Jeffreys divergence, which avoids mode dropping in high-dimensional cases. On two high-dimensional Bayesian inverse problems, we show superior pe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.05489","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2105.05489/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T02:39:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qHqKRlnZ2IJQNRFzdk4+aQJcwQ11LEUgj3xa9/bg+/CxOYDx5yWwEguGlD98cwk6R7RW+P02fi0MklSKuMtEBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T20:24:23.576845Z"},"content_sha256":"ba634c2de92fc65eadaedbdbc425588b6cb07139559dbc332ce68a2093863ba4","schema_version":"1.0","event_id":"sha256:ba634c2de92fc65eadaedbdbc425588b6cb07139559dbc332ce68a2093863ba4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PRWI7ZW4X3ID7OY7UKYVWLUCUY/bundle.json","state_url":"https://pith.science/pith/PRWI7ZW4X3ID7OY7UKYVWLUCUY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PRWI7ZW4X3ID7OY7UKYVWLUCUY/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":"2026-08-17T20:24:23Z","links":{"resolver":"https://pith.science/pith/PRWI7ZW4X3ID7OY7UKYVWLUCUY","bundle":"https://pith.science/pith/PRWI7ZW4X3ID7OY7UKYVWLUCUY/bundle.json","state":"https://pith.science/pith/PRWI7ZW4X3ID7OY7UKYVWLUCUY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PRWI7ZW4X3ID7OY7UKYVWLUCUY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:PRWI7ZW4X3ID7OY7UKYVWLUCUY","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":"2d4ce280ba972c605da8c791432425ca9bfab3a6a3aa771e0ccd2c174b393d35","cross_cats_sorted":["cs.CV","cs.LG","stat.CO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-05-12T07:51:47Z","title_canon_sha256":"8be110c4ddf2e0413c00a57a36d67895f66aa1293bfd1a0218c4d4c3d8c9b56a"},"schema_version":"1.0","source":{"id":"2105.05489","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2105.05489","created_at":"2026-07-05T02:39:51Z"},{"alias_kind":"arxiv_version","alias_value":"2105.05489v1","created_at":"2026-07-05T02:39:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.05489","created_at":"2026-07-05T02:39:51Z"},{"alias_kind":"pith_short_12","alias_value":"PRWI7ZW4X3ID","created_at":"2026-07-05T02:39:51Z"},{"alias_kind":"pith_short_16","alias_value":"PRWI7ZW4X3ID7OY7","created_at":"2026-07-05T02:39:51Z"},{"alias_kind":"pith_short_8","alias_value":"PRWI7ZW4","created_at":"2026-07-05T02:39:51Z"}],"graph_snapshots":[{"event_id":"sha256:ba634c2de92fc65eadaedbdbc425588b6cb07139559dbc332ce68a2093863ba4","target":"graph","created_at":"2026-07-05T02:39:51Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2105.05489/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose a Multiscale Invertible Generative Network (MsIGN) and associated training algorithm that leverages multiscale structure to solve high-dimensional Bayesian inference. To address the curse of dimensionality, MsIGN exploits the low-dimensional nature of the posterior, and generates samples from coarse to fine scale (low to high dimension) by iteratively upsampling and refining samples. MsIGN is trained in a multi-stage manner to minimize the Jeffreys divergence, which avoids mode dropping in high-dimensional cases. On two high-dimensional Bayesian inverse problems, we show superior pe","authors_text":"Pengchuan Zhang, Shumao Zhang, Thomas Y. Hou","cross_cats":["cs.CV","cs.LG","stat.CO"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-05-12T07:51:47Z","title":"Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.05489","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":"sha256:575da6084c8d026c24c119b836699c9ee16d6260d0803911b0881fabd18efe32","target":"record","created_at":"2026-07-05T02:39:51Z","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":"2d4ce280ba972c605da8c791432425ca9bfab3a6a3aa771e0ccd2c174b393d35","cross_cats_sorted":["cs.CV","cs.LG","stat.CO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-05-12T07:51:47Z","title_canon_sha256":"8be110c4ddf2e0413c00a57a36d67895f66aa1293bfd1a0218c4d4c3d8c9b56a"},"schema_version":"1.0","source":{"id":"2105.05489","kind":"arxiv","version":1}},"canonical_sha256":"7c6c8fe6dcbed03fbb1fa2b15b2e82a611b73f710a534920bcd371028a2bd58f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7c6c8fe6dcbed03fbb1fa2b15b2e82a611b73f710a534920bcd371028a2bd58f","first_computed_at":"2026-07-05T02:39:51.475548Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:39:51.475548Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KwQMw3GPP7BiwhAiMc7WBIjXFsSweAvA0ltVW0VcT3w4i6LObhnmCXrgyQbE+yNevU2SfBpihII5rJcFxR+jCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T02:39:51.475983Z","signed_message":"canonical_sha256_bytes"},"source_id":"2105.05489","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:575da6084c8d026c24c119b836699c9ee16d6260d0803911b0881fabd18efe32","sha256:ba634c2de92fc65eadaedbdbc425588b6cb07139559dbc332ce68a2093863ba4"],"state_sha256":"6eabb5d5b2538a4a54ad2d4836a35348702eb0345acc399aa4b6b540efdc5e13"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wT0oxYaA/YBBuYyDvufok0PRyQMI8990+OVCaamCO64wnWfmiauzrCLpNTvTZnwf1248/GXK92Vc5hW8iqeeCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T20:24:23.584357Z","bundle_sha256":"d79ea0092bc59d076906a659480bf7e34ade4f59e49cabd0e1d67f103e1d74bc"}}