{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:73JFKBKXDJ7DPRNKSMJH2NCMTM","short_pith_number":"pith:73JFKBKX","canonical_record":{"source":{"id":"2401.16497","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-01-29T19:11:03Z","cross_cats_sorted":[],"title_canon_sha256":"ed120eb1f04cf743625ea94005e433542fc5f60f2d51321c7985fed2da7bb16d","abstract_canon_sha256":"ff77549c33f80c9863808b72d4d55b05fce397fe2b7f53b1df80b3ddd9eb781d"},"schema_version":"1.0"},"canonical_sha256":"fed25505571a7e37c5aa93127d344c9b002109499dbec0739f316cb5d1f8f4e7","source":{"kind":"arxiv","id":"2401.16497","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.16497","created_at":"2026-07-05T08:58:42Z"},{"alias_kind":"arxiv_version","alias_value":"2401.16497v3","created_at":"2026-07-05T08:58:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.16497","created_at":"2026-07-05T08:58:42Z"},{"alias_kind":"pith_short_12","alias_value":"73JFKBKXDJ7D","created_at":"2026-07-05T08:58:42Z"},{"alias_kind":"pith_short_16","alias_value":"73JFKBKXDJ7DPRNK","created_at":"2026-07-05T08:58:42Z"},{"alias_kind":"pith_short_8","alias_value":"73JFKBKX","created_at":"2026-07-05T08:58:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:73JFKBKXDJ7DPRNKSMJH2NCMTM","target":"record","payload":{"canonical_record":{"source":{"id":"2401.16497","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-01-29T19:11:03Z","cross_cats_sorted":[],"title_canon_sha256":"ed120eb1f04cf743625ea94005e433542fc5f60f2d51321c7985fed2da7bb16d","abstract_canon_sha256":"ff77549c33f80c9863808b72d4d55b05fce397fe2b7f53b1df80b3ddd9eb781d"},"schema_version":"1.0"},"canonical_sha256":"fed25505571a7e37c5aa93127d344c9b002109499dbec0739f316cb5d1f8f4e7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:58:42.305109Z","signature_b64":"ZbQHPkJAm+dEGMXPRMq+0q2tNvTHNAaw9UHV2IvAMJ4M/8epjKKX8Ne24n/2znQmguaNQwjrCg9AJM7lcUwUDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fed25505571a7e37c5aa93127d344c9b002109499dbec0739f316cb5d1f8f4e7","last_reissued_at":"2026-07-05T08:58:42.304575Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:58:42.304575Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2401.16497","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:58:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BTSPPkrmxVv9bNzMuTU7a+4aDlmVTPi5nCMm7daCIjM3YUozlqNW0NJUQekjIeCmWHSGDup1gcSA4vE5e0HRAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T01:26:13.987531Z"},"content_sha256":"99a63e979452dece4db5b30b9778acdac90e38bc49eda3c1adeabdbfaefce351","schema_version":"1.0","event_id":"sha256:99a63e979452dece4db5b30b9778acdac90e38bc49eda3c1adeabdbfaefce351"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:73JFKBKXDJ7DPRNKSMJH2NCMTM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Bayesian Gaussian Process-Based Latent Discriminative Generative Decoder (LDGD) Model for High-Dimensional Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alik Widge, Ali Yousefi, Behzad Nazari, Navid Ziaei, Uri T. Eden","submitted_at":"2024-01-29T19:11:03Z","abstract_excerpt":"Extracting meaningful information from high-dimensional data poses a formidable modeling challenge, particularly when the data is obscured by noise or represented through different modalities. This research proposes a novel non-parametric modeling approach, leveraging the Gaussian process (GP), to characterize high-dimensional data by mapping it to a latent low-dimensional manifold. This model, named the latent discriminative generative decoder (LDGD), employs both the data and associated labels in the manifold discovery process. We derive a Bayesian solution to infer the latent variables, all"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.16497","kind":"arxiv","version":3},"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/2401.16497/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":"2026-07-05T08:58:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fFbXknI+Q2n0wuCLvsZhcLSzMIsXPH05ur123tHEYtlZLNKWhB0yFtN0Vhf0sndg/BwA+YWIsxCKbV99dKHeCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T01:26:13.988019Z"},"content_sha256":"b885918584018176c7d7bbf4bc526f1edc67102a6fe1396757c711aa3b456aee","schema_version":"1.0","event_id":"sha256:b885918584018176c7d7bbf4bc526f1edc67102a6fe1396757c711aa3b456aee"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/73JFKBKXDJ7DPRNKSMJH2NCMTM/bundle.json","state_url":"https://pith.science/pith/73JFKBKXDJ7DPRNKSMJH2NCMTM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/73JFKBKXDJ7DPRNKSMJH2NCMTM/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":"2026-08-23T01:26:13Z","links":{"resolver":"https://pith.science/pith/73JFKBKXDJ7DPRNKSMJH2NCMTM","bundle":"https://pith.science/pith/73JFKBKXDJ7DPRNKSMJH2NCMTM/bundle.json","state":"https://pith.science/pith/73JFKBKXDJ7DPRNKSMJH2NCMTM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/73JFKBKXDJ7DPRNKSMJH2NCMTM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:73JFKBKXDJ7DPRNKSMJH2NCMTM","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":"ff77549c33f80c9863808b72d4d55b05fce397fe2b7f53b1df80b3ddd9eb781d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-01-29T19:11:03Z","title_canon_sha256":"ed120eb1f04cf743625ea94005e433542fc5f60f2d51321c7985fed2da7bb16d"},"schema_version":"1.0","source":{"id":"2401.16497","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.16497","created_at":"2026-07-05T08:58:42Z"},{"alias_kind":"arxiv_version","alias_value":"2401.16497v3","created_at":"2026-07-05T08:58:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.16497","created_at":"2026-07-05T08:58:42Z"},{"alias_kind":"pith_short_12","alias_value":"73JFKBKXDJ7D","created_at":"2026-07-05T08:58:42Z"},{"alias_kind":"pith_short_16","alias_value":"73JFKBKXDJ7DPRNK","created_at":"2026-07-05T08:58:42Z"},{"alias_kind":"pith_short_8","alias_value":"73JFKBKX","created_at":"2026-07-05T08:58:42Z"}],"graph_snapshots":[{"event_id":"sha256:b885918584018176c7d7bbf4bc526f1edc67102a6fe1396757c711aa3b456aee","target":"graph","created_at":"2026-07-05T08:58:42Z","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/2401.16497/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Extracting meaningful information from high-dimensional data poses a formidable modeling challenge, particularly when the data is obscured by noise or represented through different modalities. This research proposes a novel non-parametric modeling approach, leveraging the Gaussian process (GP), to characterize high-dimensional data by mapping it to a latent low-dimensional manifold. This model, named the latent discriminative generative decoder (LDGD), employs both the data and associated labels in the manifold discovery process. We derive a Bayesian solution to infer the latent variables, all","authors_text":"Alik Widge, Ali Yousefi, Behzad Nazari, Navid Ziaei, Uri T. Eden","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-01-29T19:11:03Z","title":"A Bayesian Gaussian Process-Based Latent Discriminative Generative Decoder (LDGD) Model for High-Dimensional Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.16497","kind":"arxiv","version":3},"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":"sha256:99a63e979452dece4db5b30b9778acdac90e38bc49eda3c1adeabdbfaefce351","target":"record","created_at":"2026-07-05T08:58:42Z","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":"ff77549c33f80c9863808b72d4d55b05fce397fe2b7f53b1df80b3ddd9eb781d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-01-29T19:11:03Z","title_canon_sha256":"ed120eb1f04cf743625ea94005e433542fc5f60f2d51321c7985fed2da7bb16d"},"schema_version":"1.0","source":{"id":"2401.16497","kind":"arxiv","version":3}},"canonical_sha256":"fed25505571a7e37c5aa93127d344c9b002109499dbec0739f316cb5d1f8f4e7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fed25505571a7e37c5aa93127d344c9b002109499dbec0739f316cb5d1f8f4e7","first_computed_at":"2026-07-05T08:58:42.304575Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:58:42.304575Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZbQHPkJAm+dEGMXPRMq+0q2tNvTHNAaw9UHV2IvAMJ4M/8epjKKX8Ne24n/2znQmguaNQwjrCg9AJM7lcUwUDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:58:42.305109Z","signed_message":"canonical_sha256_bytes"},"source_id":"2401.16497","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:99a63e979452dece4db5b30b9778acdac90e38bc49eda3c1adeabdbfaefce351","sha256:b885918584018176c7d7bbf4bc526f1edc67102a6fe1396757c711aa3b456aee"],"state_sha256":"5db0661b16e1fa82ce05dc7b5f9610f07ac66f65487a5d290d49454cb9017421"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"68nfmshMANS7Kvzbn3HGSHeVIupeoBvOjcQNNxBsKPMkyzIzVm08/eex0bZrllo4wedbDPFd4Ahp4eeAsYjyDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T01:26:13.992476Z","bundle_sha256":"6290f6be57cce1285a2a7995527df1dacf42679c0f9b4834c0a2b99659ecc40f"}}