{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:BCND3BZXFITWPAVKNAFQTMWZ6T","short_pith_number":"pith:BCND3BZX","canonical_record":{"source":{"id":"1611.05148","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-16T05:19:50Z","cross_cats_sorted":[],"title_canon_sha256":"700ffaf5350d6ca012f7de47735005fa265a6937112f3cff4b8ae2c1805f0024","abstract_canon_sha256":"6e2471b0847dd880ac3b818a5dfacd965aa6beff2d6ba2dee70a3ec8a1bd2c2f"},"schema_version":"1.0"},"canonical_sha256":"089a3d87372a276782aa680b09b2d9f4d879dab0a25e831523a5c26c349a8e3f","source":{"kind":"arxiv","id":"1611.05148","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.05148","created_at":"2026-05-18T00:41:20Z"},{"alias_kind":"arxiv_version","alias_value":"1611.05148v3","created_at":"2026-05-18T00:41:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.05148","created_at":"2026-05-18T00:41:20Z"},{"alias_kind":"pith_short_12","alias_value":"BCND3BZXFITW","created_at":"2026-05-18T12:30:07Z"},{"alias_kind":"pith_short_16","alias_value":"BCND3BZXFITWPAVK","created_at":"2026-05-18T12:30:07Z"},{"alias_kind":"pith_short_8","alias_value":"BCND3BZX","created_at":"2026-05-18T12:30:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:BCND3BZXFITWPAVKNAFQTMWZ6T","target":"record","payload":{"canonical_record":{"source":{"id":"1611.05148","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-16T05:19:50Z","cross_cats_sorted":[],"title_canon_sha256":"700ffaf5350d6ca012f7de47735005fa265a6937112f3cff4b8ae2c1805f0024","abstract_canon_sha256":"6e2471b0847dd880ac3b818a5dfacd965aa6beff2d6ba2dee70a3ec8a1bd2c2f"},"schema_version":"1.0"},"canonical_sha256":"089a3d87372a276782aa680b09b2d9f4d879dab0a25e831523a5c26c349a8e3f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:41:20.970306Z","signature_b64":"w8mpbWy1cDxUucCWwT/ozl/W5xAlt4iBr9c06ZT8yulTfdh6C8oeAyjTgeK64RP3RrfZ10VbimfMgHNFSSY/Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"089a3d87372a276782aa680b09b2d9f4d879dab0a25e831523a5c26c349a8e3f","last_reissued_at":"2026-05-18T00:41:20.969647Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:41:20.969647Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1611.05148","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":"2026-05-18T00:41:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LDf5WPRZ1aOrzSNjpPCt5F5zV5gD2cQersLA0mYbQbaNzmpUk/SSSRNO+n2N806k5FFa2Z+f3uVqtvntiL7kAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T16:56:35.952346Z"},"content_sha256":"857453e0b7d3fccc3d941cc8af11d133dc24ef7a801ec7b80c0845071e7fc177","schema_version":"1.0","event_id":"sha256:857453e0b7d3fccc3d941cc8af11d133dc24ef7a801ec7b80c0845071e7fc177"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:BCND3BZXFITWPAVKNAFQTMWZ6T","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bangsheng Tang, Hanning Zhou, Huachun Tan, Yin Zheng, Zhuxi Jiang","submitted_at":"2016-11-16T05:19:50Z","abstract_excerpt":"Clustering is among the most fundamental tasks in computer vision and machine learning. In this paper, we propose Variational Deep Embedding (VaDE), a novel unsupervised generative clustering approach within the framework of Variational Auto-Encoder (VAE). Specifically, VaDE models the data generative procedure with a Gaussian Mixture Model (GMM) and a deep neural network (DNN): 1) the GMM picks a cluster; 2) from which a latent embedding is generated; 3) then the DNN decodes the latent embedding into observables. Inference in VaDE is done in a variational way: a different DNN is used to encod"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.05148","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":""},"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":"2026-05-18T00:41:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AmNzkk2f0+Wq49CjyzdEJz5on+DtQiG916evMgHyXgXtLuKOaNoV/ySiOtMYPatWSJr+pOwWHp9a9KntWJpWCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T16:56:35.952709Z"},"content_sha256":"5640a4b892b2281e9ba76557794fc3618b7c06718da887bcbf2525ff649392e9","schema_version":"1.0","event_id":"sha256:5640a4b892b2281e9ba76557794fc3618b7c06718da887bcbf2525ff649392e9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BCND3BZXFITWPAVKNAFQTMWZ6T/bundle.json","state_url":"https://pith.science/pith/BCND3BZXFITWPAVKNAFQTMWZ6T/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BCND3BZXFITWPAVKNAFQTMWZ6T/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":"2026-05-26T16:56:35Z","links":{"resolver":"https://pith.science/pith/BCND3BZXFITWPAVKNAFQTMWZ6T","bundle":"https://pith.science/pith/BCND3BZXFITWPAVKNAFQTMWZ6T/bundle.json","state":"https://pith.science/pith/BCND3BZXFITWPAVKNAFQTMWZ6T/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BCND3BZXFITWPAVKNAFQTMWZ6T/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:BCND3BZXFITWPAVKNAFQTMWZ6T","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":"6e2471b0847dd880ac3b818a5dfacd965aa6beff2d6ba2dee70a3ec8a1bd2c2f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-16T05:19:50Z","title_canon_sha256":"700ffaf5350d6ca012f7de47735005fa265a6937112f3cff4b8ae2c1805f0024"},"schema_version":"1.0","source":{"id":"1611.05148","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.05148","created_at":"2026-05-18T00:41:20Z"},{"alias_kind":"arxiv_version","alias_value":"1611.05148v3","created_at":"2026-05-18T00:41:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.05148","created_at":"2026-05-18T00:41:20Z"},{"alias_kind":"pith_short_12","alias_value":"BCND3BZXFITW","created_at":"2026-05-18T12:30:07Z"},{"alias_kind":"pith_short_16","alias_value":"BCND3BZXFITWPAVK","created_at":"2026-05-18T12:30:07Z"},{"alias_kind":"pith_short_8","alias_value":"BCND3BZX","created_at":"2026-05-18T12:30:07Z"}],"graph_snapshots":[{"event_id":"sha256:5640a4b892b2281e9ba76557794fc3618b7c06718da887bcbf2525ff649392e9","target":"graph","created_at":"2026-05-18T00:41:20Z","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"},"paper":{"abstract_excerpt":"Clustering is among the most fundamental tasks in computer vision and machine learning. In this paper, we propose Variational Deep Embedding (VaDE), a novel unsupervised generative clustering approach within the framework of Variational Auto-Encoder (VAE). Specifically, VaDE models the data generative procedure with a Gaussian Mixture Model (GMM) and a deep neural network (DNN): 1) the GMM picks a cluster; 2) from which a latent embedding is generated; 3) then the DNN decodes the latent embedding into observables. Inference in VaDE is done in a variational way: a different DNN is used to encod","authors_text":"Bangsheng Tang, Hanning Zhou, Huachun Tan, Yin Zheng, Zhuxi Jiang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-16T05:19:50Z","title":"Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.05148","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":"sha256:857453e0b7d3fccc3d941cc8af11d133dc24ef7a801ec7b80c0845071e7fc177","target":"record","created_at":"2026-05-18T00:41:20Z","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":"6e2471b0847dd880ac3b818a5dfacd965aa6beff2d6ba2dee70a3ec8a1bd2c2f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-16T05:19:50Z","title_canon_sha256":"700ffaf5350d6ca012f7de47735005fa265a6937112f3cff4b8ae2c1805f0024"},"schema_version":"1.0","source":{"id":"1611.05148","kind":"arxiv","version":3}},"canonical_sha256":"089a3d87372a276782aa680b09b2d9f4d879dab0a25e831523a5c26c349a8e3f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"089a3d87372a276782aa680b09b2d9f4d879dab0a25e831523a5c26c349a8e3f","first_computed_at":"2026-05-18T00:41:20.969647Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:41:20.969647Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"w8mpbWy1cDxUucCWwT/ozl/W5xAlt4iBr9c06ZT8yulTfdh6C8oeAyjTgeK64RP3RrfZ10VbimfMgHNFSSY/Aw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:41:20.970306Z","signed_message":"canonical_sha256_bytes"},"source_id":"1611.05148","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:857453e0b7d3fccc3d941cc8af11d133dc24ef7a801ec7b80c0845071e7fc177","sha256:5640a4b892b2281e9ba76557794fc3618b7c06718da887bcbf2525ff649392e9"],"state_sha256":"1b08caaa15915e379700317765387aa4401c92e60f95ea3da88d8afe6327b57b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"987eCDt90YA7vxqZ/XyQ29cHsr1B2u6sD2NwUCQQFIxfmCIdOCQJblhNfNVzFoZsWg6m4Sg/Uc4qbGGxkCs5CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T16:56:35.955198Z","bundle_sha256":"7f1c1ef9ebe6673db28cfb48a17615485e314ef57369df308a20cc58a977b561"}}