{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:ZAFKG3C52ZW6JIY2Q4PPXIU3AH","short_pith_number":"pith:ZAFKG3C5","canonical_record":{"source":{"id":"2302.05917","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-12T13:51:36Z","cross_cats_sorted":[],"title_canon_sha256":"2ea5564996a5d24b2636593834386aab3dd6f63a6b072f0abf4ec80718fe6fbc","abstract_canon_sha256":"c551d1495c728c014cefab1b366fb4a59e67d4ef26c83650fc72c236f506571d"},"schema_version":"1.0"},"canonical_sha256":"c80aa36c5dd66de4a31a871efba29b01fce76981ddd65749d511fecfed1eddfb","source":{"kind":"arxiv","id":"2302.05917","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.05917","created_at":"2026-07-05T06:21:30Z"},{"alias_kind":"arxiv_version","alias_value":"2302.05917v2","created_at":"2026-07-05T06:21:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.05917","created_at":"2026-07-05T06:21:30Z"},{"alias_kind":"pith_short_12","alias_value":"ZAFKG3C52ZW6","created_at":"2026-07-05T06:21:30Z"},{"alias_kind":"pith_short_16","alias_value":"ZAFKG3C52ZW6JIY2","created_at":"2026-07-05T06:21:30Z"},{"alias_kind":"pith_short_8","alias_value":"ZAFKG3C5","created_at":"2026-07-05T06:21:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:ZAFKG3C52ZW6JIY2Q4PPXIU3AH","target":"record","payload":{"canonical_record":{"source":{"id":"2302.05917","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-12T13:51:36Z","cross_cats_sorted":[],"title_canon_sha256":"2ea5564996a5d24b2636593834386aab3dd6f63a6b072f0abf4ec80718fe6fbc","abstract_canon_sha256":"c551d1495c728c014cefab1b366fb4a59e67d4ef26c83650fc72c236f506571d"},"schema_version":"1.0"},"canonical_sha256":"c80aa36c5dd66de4a31a871efba29b01fce76981ddd65749d511fecfed1eddfb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:21:30.505881Z","signature_b64":"r2lb0i4GJOA8M6JGxbZtEHmtfbRgEAb+P6VcztCqOYkcRGULyi0unyuYXYPxv9Veh9XnlLVkWmFgIM51gC6FBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c80aa36c5dd66de4a31a871efba29b01fce76981ddd65749d511fecfed1eddfb","last_reissued_at":"2026-07-05T06:21:30.505450Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:21:30.505450Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2302.05917","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:21:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gJTZijChYKIBFg46jfOy8DBnLC/2dB9TEcTZw4wGHZm1CUF9EHOrN4CGxPOqwS1wuh9YS4qAv1hubcxdxIxUBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T12:16:25.217108Z"},"content_sha256":"4d74f459ba1cdfe2da513d3409a42d5f177ef025021d5b14d09a2f86db7fdf01","schema_version":"1.0","event_id":"sha256:4d74f459ba1cdfe2da513d3409a42d5f177ef025021d5b14d09a2f86db7fdf01"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:ZAFKG3C52ZW6JIY2Q4PPXIU3AH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Vector Quantized Wasserstein Auto-Encoder","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chuanxia Zheng, Dinh Phung, He Zhao, Jianfei Cai, Mehrtash Harandi, Trung Le, Tung-Long Vuong","submitted_at":"2023-02-12T13:51:36Z","abstract_excerpt":"Learning deep discrete latent presentations offers a promise of better symbolic and summarized abstractions that are more useful to subsequent downstream tasks. Inspired by the seminal Vector Quantized Variational Auto-Encoder (VQ-VAE), most of work in learning deep discrete representations has mainly focused on improving the original VQ-VAE form and none of them has studied learning deep discrete representations from the generative viewpoint. In this work, we study learning deep discrete representations from the generative viewpoint. Specifically, we endow discrete distributions over sequence"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.05917","kind":"arxiv","version":2},"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/2302.05917/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":"2026-07-05T06:21:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UM7Ja4HAW35BWjbo8b+YtJqXI84sjZxk0saM+yvvnGBqtasmGtjHhtOZpxIMTsw/UqucJDKCw1L28X30TtYyBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T12:16:25.217599Z"},"content_sha256":"52151c7ab1de347d690bb26100844343f6193d002322c4cabd0c19c04550a0fa","schema_version":"1.0","event_id":"sha256:52151c7ab1de347d690bb26100844343f6193d002322c4cabd0c19c04550a0fa"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZAFKG3C52ZW6JIY2Q4PPXIU3AH/bundle.json","state_url":"https://pith.science/pith/ZAFKG3C52ZW6JIY2Q4PPXIU3AH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZAFKG3C52ZW6JIY2Q4PPXIU3AH/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":"2026-08-09T12:16:25Z","links":{"resolver":"https://pith.science/pith/ZAFKG3C52ZW6JIY2Q4PPXIU3AH","bundle":"https://pith.science/pith/ZAFKG3C52ZW6JIY2Q4PPXIU3AH/bundle.json","state":"https://pith.science/pith/ZAFKG3C52ZW6JIY2Q4PPXIU3AH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZAFKG3C52ZW6JIY2Q4PPXIU3AH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:ZAFKG3C52ZW6JIY2Q4PPXIU3AH","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":"c551d1495c728c014cefab1b366fb4a59e67d4ef26c83650fc72c236f506571d","cross_cats_sorted":[],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-12T13:51:36Z","title_canon_sha256":"2ea5564996a5d24b2636593834386aab3dd6f63a6b072f0abf4ec80718fe6fbc"},"schema_version":"1.0","source":{"id":"2302.05917","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.05917","created_at":"2026-07-05T06:21:30Z"},{"alias_kind":"arxiv_version","alias_value":"2302.05917v2","created_at":"2026-07-05T06:21:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.05917","created_at":"2026-07-05T06:21:30Z"},{"alias_kind":"pith_short_12","alias_value":"ZAFKG3C52ZW6","created_at":"2026-07-05T06:21:30Z"},{"alias_kind":"pith_short_16","alias_value":"ZAFKG3C52ZW6JIY2","created_at":"2026-07-05T06:21:30Z"},{"alias_kind":"pith_short_8","alias_value":"ZAFKG3C5","created_at":"2026-07-05T06:21:30Z"}],"graph_snapshots":[{"event_id":"sha256:52151c7ab1de347d690bb26100844343f6193d002322c4cabd0c19c04550a0fa","target":"graph","created_at":"2026-07-05T06:21:30Z","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/2302.05917/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Learning deep discrete latent presentations offers a promise of better symbolic and summarized abstractions that are more useful to subsequent downstream tasks. Inspired by the seminal Vector Quantized Variational Auto-Encoder (VQ-VAE), most of work in learning deep discrete representations has mainly focused on improving the original VQ-VAE form and none of them has studied learning deep discrete representations from the generative viewpoint. In this work, we study learning deep discrete representations from the generative viewpoint. Specifically, we endow discrete distributions over sequence","authors_text":"Chuanxia Zheng, Dinh Phung, He Zhao, Jianfei Cai, Mehrtash Harandi, Trung Le, Tung-Long Vuong","cross_cats":[],"headline":"","license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-12T13:51:36Z","title":"Vector Quantized Wasserstein Auto-Encoder"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.05917","kind":"arxiv","version":2},"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":"sha256:4d74f459ba1cdfe2da513d3409a42d5f177ef025021d5b14d09a2f86db7fdf01","target":"record","created_at":"2026-07-05T06:21:30Z","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":"c551d1495c728c014cefab1b366fb4a59e67d4ef26c83650fc72c236f506571d","cross_cats_sorted":[],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-12T13:51:36Z","title_canon_sha256":"2ea5564996a5d24b2636593834386aab3dd6f63a6b072f0abf4ec80718fe6fbc"},"schema_version":"1.0","source":{"id":"2302.05917","kind":"arxiv","version":2}},"canonical_sha256":"c80aa36c5dd66de4a31a871efba29b01fce76981ddd65749d511fecfed1eddfb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c80aa36c5dd66de4a31a871efba29b01fce76981ddd65749d511fecfed1eddfb","first_computed_at":"2026-07-05T06:21:30.505450Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:21:30.505450Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"r2lb0i4GJOA8M6JGxbZtEHmtfbRgEAb+P6VcztCqOYkcRGULyi0unyuYXYPxv9Veh9XnlLVkWmFgIM51gC6FBg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:21:30.505881Z","signed_message":"canonical_sha256_bytes"},"source_id":"2302.05917","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4d74f459ba1cdfe2da513d3409a42d5f177ef025021d5b14d09a2f86db7fdf01","sha256:52151c7ab1de347d690bb26100844343f6193d002322c4cabd0c19c04550a0fa"],"state_sha256":"c0e19d29e720c542bd18b67e974db242aec03861f1253cdf8247947d75be6b09"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LClg4xnGfRvZW7koMt7m9FLQVB10UPZ48wvJVZ/crfs0Wu4O9E+lNy+nj/0e9ghZM3C0ZtELZjdHLK8u3/sJCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T12:16:25.222513Z","bundle_sha256":"1b0593a1a1446d4dadceb8ae89411c26bfe090610064a246ee3fac4aef830bd3"}}