{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:THOBBT22RQZSRLJBNNLK5IG2HK","short_pith_number":"pith:THOBBT22","canonical_record":{"source":{"id":"1911.05343","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-11-13T08:11:42Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"9c1f26b81d3f24cb0decb8ca46867b044066d63c051ced3b057f3c5dabbd838d","abstract_canon_sha256":"7030a49753e44626cb040a3ea4536dd33d31749e6d7c018403315e3b42616aa3"},"schema_version":"1.0"},"canonical_sha256":"99dc10cf5a8c3328ad216b56aea0da3aa6057421d4bec1381f0161b925dba43e","source":{"kind":"arxiv","id":"1911.05343","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1911.05343","created_at":"2026-07-05T00:18:56Z"},{"alias_kind":"arxiv_version","alias_value":"1911.05343v1","created_at":"2026-07-05T00:18:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.05343","created_at":"2026-07-05T00:18:56Z"},{"alias_kind":"pith_short_12","alias_value":"THOBBT22RQZS","created_at":"2026-07-05T00:18:56Z"},{"alias_kind":"pith_short_16","alias_value":"THOBBT22RQZSRLJB","created_at":"2026-07-05T00:18:56Z"},{"alias_kind":"pith_short_8","alias_value":"THOBBT22","created_at":"2026-07-05T00:18:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:THOBBT22RQZSRLJBNNLK5IG2HK","target":"record","payload":{"canonical_record":{"source":{"id":"1911.05343","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-11-13T08:11:42Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"9c1f26b81d3f24cb0decb8ca46867b044066d63c051ced3b057f3c5dabbd838d","abstract_canon_sha256":"7030a49753e44626cb040a3ea4536dd33d31749e6d7c018403315e3b42616aa3"},"schema_version":"1.0"},"canonical_sha256":"99dc10cf5a8c3328ad216b56aea0da3aa6057421d4bec1381f0161b925dba43e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:18:56.250709Z","signature_b64":"U1EnSVj1jb6zsje96K871OaQow1w7t5tiXqHW9uo0ZHbU8DlkCVOd93mOmOq3qcdd9hE0WHQo53qlESImD30Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"99dc10cf5a8c3328ad216b56aea0da3aa6057421d4bec1381f0161b925dba43e","last_reissued_at":"2026-07-05T00:18:56.250290Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:18:56.250290Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1911.05343","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":"2026-07-05T00:18:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vXqKXOyvwrz64HmG4NwASOUmMrXasKCf2J33Hzy833swZgtTYWyU97oiZZi9l4GdX4VEI+BP+WQj+/g+77h/AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T13:29:51.521519Z"},"content_sha256":"3df0c66b5b8e06641f4936ed5df92f204a8c96837ab513992a5c23c905cc3c63","schema_version":"1.0","event_id":"sha256:3df0c66b5b8e06641f4936ed5df92f204a8c96837ab513992a5c23c905cc3c63"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:THOBBT22RQZSRLJBNNLK5IG2HK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Stable Variational Autoencoder for Text Modelling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Chenghua Lin, Matthew Collinson, Rui Mao, Ruizhe Li, Xiao Li","submitted_at":"2019-11-13T08:11:42Z","abstract_excerpt":"Variational Autoencoder (VAE) is a powerful method for learning representations of high-dimensional data. However, VAEs can suffer from an issue known as latent variable collapse (or KL loss vanishing), where the posterior collapses to the prior and the model will ignore the latent codes in generative tasks. Such an issue is particularly prevalent when employing VAE-RNN architectures for text modelling (Bowman et al., 2016). In this paper, we present a simple architecture called holistic regularisation VAE (HR-VAE), which can effectively avoid latent variable collapse. Compared to existing VAE"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.05343","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/1911.05343/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":"2026-07-05T00:18:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UUKb8QCxHyuwYrR6jp4n7X3mhlMhYMp/z9TRF19R1W+wsKI887oY6V8NrU2E+SJ+107qWQbjtKjjtUfOt7vDCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T13:29:51.522461Z"},"content_sha256":"a5a11cc0cc755960ac0d9a51aedc4796d0524e74a11394b86cf811aa4b0057eb","schema_version":"1.0","event_id":"sha256:a5a11cc0cc755960ac0d9a51aedc4796d0524e74a11394b86cf811aa4b0057eb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/THOBBT22RQZSRLJBNNLK5IG2HK/bundle.json","state_url":"https://pith.science/pith/THOBBT22RQZSRLJBNNLK5IG2HK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/THOBBT22RQZSRLJBNNLK5IG2HK/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":"2026-08-20T13:29:51Z","links":{"resolver":"https://pith.science/pith/THOBBT22RQZSRLJBNNLK5IG2HK","bundle":"https://pith.science/pith/THOBBT22RQZSRLJBNNLK5IG2HK/bundle.json","state":"https://pith.science/pith/THOBBT22RQZSRLJBNNLK5IG2HK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/THOBBT22RQZSRLJBNNLK5IG2HK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:THOBBT22RQZSRLJBNNLK5IG2HK","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":"7030a49753e44626cb040a3ea4536dd33d31749e6d7c018403315e3b42616aa3","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-11-13T08:11:42Z","title_canon_sha256":"9c1f26b81d3f24cb0decb8ca46867b044066d63c051ced3b057f3c5dabbd838d"},"schema_version":"1.0","source":{"id":"1911.05343","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1911.05343","created_at":"2026-07-05T00:18:56Z"},{"alias_kind":"arxiv_version","alias_value":"1911.05343v1","created_at":"2026-07-05T00:18:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.05343","created_at":"2026-07-05T00:18:56Z"},{"alias_kind":"pith_short_12","alias_value":"THOBBT22RQZS","created_at":"2026-07-05T00:18:56Z"},{"alias_kind":"pith_short_16","alias_value":"THOBBT22RQZSRLJB","created_at":"2026-07-05T00:18:56Z"},{"alias_kind":"pith_short_8","alias_value":"THOBBT22","created_at":"2026-07-05T00:18:56Z"}],"graph_snapshots":[{"event_id":"sha256:a5a11cc0cc755960ac0d9a51aedc4796d0524e74a11394b86cf811aa4b0057eb","target":"graph","created_at":"2026-07-05T00:18:56Z","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/1911.05343/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Variational Autoencoder (VAE) is a powerful method for learning representations of high-dimensional data. However, VAEs can suffer from an issue known as latent variable collapse (or KL loss vanishing), where the posterior collapses to the prior and the model will ignore the latent codes in generative tasks. Such an issue is particularly prevalent when employing VAE-RNN architectures for text modelling (Bowman et al., 2016). In this paper, we present a simple architecture called holistic regularisation VAE (HR-VAE), which can effectively avoid latent variable collapse. Compared to existing VAE","authors_text":"Chenghua Lin, Matthew Collinson, Rui Mao, Ruizhe Li, Xiao Li","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-11-13T08:11:42Z","title":"A Stable Variational Autoencoder for Text Modelling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.05343","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":"sha256:3df0c66b5b8e06641f4936ed5df92f204a8c96837ab513992a5c23c905cc3c63","target":"record","created_at":"2026-07-05T00:18:56Z","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":"7030a49753e44626cb040a3ea4536dd33d31749e6d7c018403315e3b42616aa3","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-11-13T08:11:42Z","title_canon_sha256":"9c1f26b81d3f24cb0decb8ca46867b044066d63c051ced3b057f3c5dabbd838d"},"schema_version":"1.0","source":{"id":"1911.05343","kind":"arxiv","version":1}},"canonical_sha256":"99dc10cf5a8c3328ad216b56aea0da3aa6057421d4bec1381f0161b925dba43e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"99dc10cf5a8c3328ad216b56aea0da3aa6057421d4bec1381f0161b925dba43e","first_computed_at":"2026-07-05T00:18:56.250290Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:18:56.250290Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"U1EnSVj1jb6zsje96K871OaQow1w7t5tiXqHW9uo0ZHbU8DlkCVOd93mOmOq3qcdd9hE0WHQo53qlESImD30Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:18:56.250709Z","signed_message":"canonical_sha256_bytes"},"source_id":"1911.05343","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3df0c66b5b8e06641f4936ed5df92f204a8c96837ab513992a5c23c905cc3c63","sha256:a5a11cc0cc755960ac0d9a51aedc4796d0524e74a11394b86cf811aa4b0057eb"],"state_sha256":"17584571192a1adb6e8115c08d603f834b619d6bed7ef6e4d57860efdf0ad0e6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"p4AVNEW6cdB/bbTXPe6LUbT/XRT+97pTzUVecLJTIKBiboqh7KSENVW6LSvdBomHq9szFWWiUc6tMUL4oMX+Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T13:29:51.529077Z","bundle_sha256":"2fe9a9248766b7a91b0754478fcfa012df67f3b20045437406200d8824b0ae45"}}