{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:63MYTV44TWDN6DL6N5D43NERKP","short_pith_number":"pith:63MYTV44","canonical_record":{"source":{"id":"2505.18558","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-24T06:52:23Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"7005e975a7081138b8e89e2b71a157961e76bf7610d6e7c3ebc080b04e26087d","abstract_canon_sha256":"46f75ac0ce2d29878e47c02451db894999ba3b97dd7c1b8c98c22450be8ff42a"},"schema_version":"1.0"},"canonical_sha256":"f6d989d79c9d86df0d7e6f47cdb49153fb65b3a3362b97514a765600fd68cc1a","source":{"kind":"arxiv","id":"2505.18558","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.18558","created_at":"2026-07-05T11:08:48Z"},{"alias_kind":"arxiv_version","alias_value":"2505.18558v1","created_at":"2026-07-05T11:08:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.18558","created_at":"2026-07-05T11:08:48Z"},{"alias_kind":"pith_short_12","alias_value":"63MYTV44TWDN","created_at":"2026-07-05T11:08:48Z"},{"alias_kind":"pith_short_16","alias_value":"63MYTV44TWDN6DL6","created_at":"2026-07-05T11:08:48Z"},{"alias_kind":"pith_short_8","alias_value":"63MYTV44","created_at":"2026-07-05T11:08:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:63MYTV44TWDN6DL6N5D43NERKP","target":"record","payload":{"canonical_record":{"source":{"id":"2505.18558","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-24T06:52:23Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"7005e975a7081138b8e89e2b71a157961e76bf7610d6e7c3ebc080b04e26087d","abstract_canon_sha256":"46f75ac0ce2d29878e47c02451db894999ba3b97dd7c1b8c98c22450be8ff42a"},"schema_version":"1.0"},"canonical_sha256":"f6d989d79c9d86df0d7e6f47cdb49153fb65b3a3362b97514a765600fd68cc1a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:08:48.391842Z","signature_b64":"ze/NuBHzgnDILGq9m4ZfpOq7/pcyfiul2b7glC63uyZvH0x1PSJTZTUNec53YMe9nez0VrcSP8vTFQAimKJmBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f6d989d79c9d86df0d7e6f47cdb49153fb65b3a3362b97514a765600fd68cc1a","last_reissued_at":"2026-07-05T11:08:48.391300Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:08:48.391300Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.18558","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":"2026-07-05T11:08:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NCcZd7+5fu6+l5UTy0vZwwzOmZsjkO0umK01XUXYqxbviid4xDE/5GJ3KwzY4wM5wJznWQm15TrUIFwFOKwQCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T08:34:32.453995Z"},"content_sha256":"6df9fcbca1db3ffbfdac8436b4c90838edd03818b3c11c65d5e18b1980320364","schema_version":"1.0","event_id":"sha256:6df9fcbca1db3ffbfdac8436b4c90838edd03818b3c11c65d5e18b1980320364"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:63MYTV44TWDN6DL6N5D43NERKP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Joint-stochastic-approximation Autoencoders with Application to Semi-supervised Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Wenbo He, Zhijian Ou","submitted_at":"2025-05-24T06:52:23Z","abstract_excerpt":"Our examination of existing deep generative models (DGMs), including VAEs and GANs, reveals two problems. First, their capability in handling discrete observations and latent codes is unsatisfactory, though there are interesting efforts. Second, both VAEs and GANs optimize some criteria that are indirectly related to the data likelihood. To address these problems, we formally present Joint-stochastic-approximation (JSA) autoencoders - a new family of algorithms for building deep directed generative models, with application to semi-supervised learning. The JSA learning algorithm directly maximi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.18558","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/2505.18558/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":"2026-07-05T11:08:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aXX695wq8feN5A3T9XO0qunjg/lx7OmkBBujMpQ3I4aFWONVF8iYq8Psiv0zTckHTrcfoR+3/HkBH5U6aJHiDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T08:34:32.454493Z"},"content_sha256":"d0e7255505b14462c7e20a84cb5aa23516203f68dbeb727539bef7ed8c3718bc","schema_version":"1.0","event_id":"sha256:d0e7255505b14462c7e20a84cb5aa23516203f68dbeb727539bef7ed8c3718bc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/63MYTV44TWDN6DL6N5D43NERKP/bundle.json","state_url":"https://pith.science/pith/63MYTV44TWDN6DL6N5D43NERKP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/63MYTV44TWDN6DL6N5D43NERKP/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":"2026-08-08T08:34:32Z","links":{"resolver":"https://pith.science/pith/63MYTV44TWDN6DL6N5D43NERKP","bundle":"https://pith.science/pith/63MYTV44TWDN6DL6N5D43NERKP/bundle.json","state":"https://pith.science/pith/63MYTV44TWDN6DL6N5D43NERKP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/63MYTV44TWDN6DL6N5D43NERKP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:63MYTV44TWDN6DL6N5D43NERKP","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":"46f75ac0ce2d29878e47c02451db894999ba3b97dd7c1b8c98c22450be8ff42a","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-24T06:52:23Z","title_canon_sha256":"7005e975a7081138b8e89e2b71a157961e76bf7610d6e7c3ebc080b04e26087d"},"schema_version":"1.0","source":{"id":"2505.18558","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.18558","created_at":"2026-07-05T11:08:48Z"},{"alias_kind":"arxiv_version","alias_value":"2505.18558v1","created_at":"2026-07-05T11:08:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.18558","created_at":"2026-07-05T11:08:48Z"},{"alias_kind":"pith_short_12","alias_value":"63MYTV44TWDN","created_at":"2026-07-05T11:08:48Z"},{"alias_kind":"pith_short_16","alias_value":"63MYTV44TWDN6DL6","created_at":"2026-07-05T11:08:48Z"},{"alias_kind":"pith_short_8","alias_value":"63MYTV44","created_at":"2026-07-05T11:08:48Z"}],"graph_snapshots":[{"event_id":"sha256:d0e7255505b14462c7e20a84cb5aa23516203f68dbeb727539bef7ed8c3718bc","target":"graph","created_at":"2026-07-05T11:08:48Z","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/2505.18558/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Our examination of existing deep generative models (DGMs), including VAEs and GANs, reveals two problems. First, their capability in handling discrete observations and latent codes is unsatisfactory, though there are interesting efforts. Second, both VAEs and GANs optimize some criteria that are indirectly related to the data likelihood. To address these problems, we formally present Joint-stochastic-approximation (JSA) autoencoders - a new family of algorithms for building deep directed generative models, with application to semi-supervised learning. The JSA learning algorithm directly maximi","authors_text":"Wenbo He, Zhijian Ou","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-24T06:52:23Z","title":"Joint-stochastic-approximation Autoencoders with Application to Semi-supervised Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.18558","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":"sha256:6df9fcbca1db3ffbfdac8436b4c90838edd03818b3c11c65d5e18b1980320364","target":"record","created_at":"2026-07-05T11:08:48Z","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":"46f75ac0ce2d29878e47c02451db894999ba3b97dd7c1b8c98c22450be8ff42a","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-24T06:52:23Z","title_canon_sha256":"7005e975a7081138b8e89e2b71a157961e76bf7610d6e7c3ebc080b04e26087d"},"schema_version":"1.0","source":{"id":"2505.18558","kind":"arxiv","version":1}},"canonical_sha256":"f6d989d79c9d86df0d7e6f47cdb49153fb65b3a3362b97514a765600fd68cc1a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f6d989d79c9d86df0d7e6f47cdb49153fb65b3a3362b97514a765600fd68cc1a","first_computed_at":"2026-07-05T11:08:48.391300Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:08:48.391300Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ze/NuBHzgnDILGq9m4ZfpOq7/pcyfiul2b7glC63uyZvH0x1PSJTZTUNec53YMe9nez0VrcSP8vTFQAimKJmBg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:08:48.391842Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.18558","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6df9fcbca1db3ffbfdac8436b4c90838edd03818b3c11c65d5e18b1980320364","sha256:d0e7255505b14462c7e20a84cb5aa23516203f68dbeb727539bef7ed8c3718bc"],"state_sha256":"9952ec606c8bbfb73102c578ed0bb18b5cab52aa0c1a3d6bc4f6d54235ab262e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QvSWjaS+EK9AU3S9XMpWHWYDRVIfGDEa6rxl5OO9h5DfwcxbXOowWLKUcOWcnozPTohTdySV++AzMpeop+hIBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T08:34:32.457838Z","bundle_sha256":"d30212642dde19a54e41a920178c445f1632087a67ea45610183b93590ca2aa1"}}