{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:WZHGXU5IFJKPQ4FLLWR2I2JXW4","short_pith_number":"pith:WZHGXU5I","canonical_record":{"source":{"id":"1611.01799","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-11-06T16:04:48Z","cross_cats_sorted":[],"title_canon_sha256":"5246233b3a6011939d97aac4e2b862867b813f17e1694a56562a1e9c5b68007b","abstract_canon_sha256":"ee3a6a69b767afbe9fa73c3fdadae0d459e6cd16d775f6326065108dfd7a4478"},"schema_version":"1.0"},"canonical_sha256":"b64e6bd3a82a54f870ab5da3a46937b714e3c9c601b177bb798dde3ab67ab42c","source":{"kind":"arxiv","id":"1611.01799","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.01799","created_at":"2026-05-18T01:00:04Z"},{"alias_kind":"arxiv_version","alias_value":"1611.01799v1","created_at":"2026-05-18T01:00:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.01799","created_at":"2026-05-18T01:00:04Z"},{"alias_kind":"pith_short_12","alias_value":"WZHGXU5IFJKP","created_at":"2026-05-18T12:30:51Z"},{"alias_kind":"pith_short_16","alias_value":"WZHGXU5IFJKPQ4FL","created_at":"2026-05-18T12:30:51Z"},{"alias_kind":"pith_short_8","alias_value":"WZHGXU5I","created_at":"2026-05-18T12:30:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:WZHGXU5IFJKPQ4FLLWR2I2JXW4","target":"record","payload":{"canonical_record":{"source":{"id":"1611.01799","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-11-06T16:04:48Z","cross_cats_sorted":[],"title_canon_sha256":"5246233b3a6011939d97aac4e2b862867b813f17e1694a56562a1e9c5b68007b","abstract_canon_sha256":"ee3a6a69b767afbe9fa73c3fdadae0d459e6cd16d775f6326065108dfd7a4478"},"schema_version":"1.0"},"canonical_sha256":"b64e6bd3a82a54f870ab5da3a46937b714e3c9c601b177bb798dde3ab67ab42c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:00:04.459950Z","signature_b64":"SZ2KbrkQ9DbztWyQwQEfJBT1iAPXie2WYohThq5yhn1dMzZxN9gn2NDXrcgY8aossUO1OHdK2JsOTRxppbJRAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b64e6bd3a82a54f870ab5da3a46937b714e3c9c601b177bb798dde3ab67ab42c","last_reissued_at":"2026-05-18T01:00:04.459343Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:00:04.459343Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1611.01799","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":"2026-05-18T01:00:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"r3FdZqsd1VjqBelEFS6NmznCRBaZwcihUb5psMuQqpvOBirVuNXSyaf7pUl6Zh8J2L5gpxDa47+yLqzFB81tBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T16:10:38.664956Z"},"content_sha256":"116792a118386ad26b17397d1aa19aa08b6ce5d9d61439b475238ecf246a1c02","schema_version":"1.0","event_id":"sha256:116792a118386ad26b17397d1aa19aa08b6ce5d9d61439b475238ecf246a1c02"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:WZHGXU5IFJKPQ4FLLWR2I2JXW4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Generative Adversarial Networks as Variational Training of Energy Based Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Rogerio Feris, Shuangfei Zhai, Yu Cheng, Zhongfei Zhang","submitted_at":"2016-11-06T16:04:48Z","abstract_excerpt":"In this paper, we study deep generative models for effective unsupervised learning. We propose VGAN, which works by minimizing a variational lower bound of the negative log likelihood (NLL) of an energy based model (EBM), where the model density $p(\\mathbf{x})$ is approximated by a variational distribution $q(\\mathbf{x})$ that is easy to sample from. The training of VGAN takes a two step procedure: given $p(\\mathbf{x})$, $q(\\mathbf{x})$ is updated to maximize the lower bound; $p(\\mathbf{x})$ is then updated one step with samples drawn from $q(\\mathbf{x})$ to decrease the lower bound. VGAN is i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.01799","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":""},"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":"2026-05-18T01:00:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7Rzttxllc/n+h/7odtLtJ5kRJZyY0BzeDRx2Nitta7WPKwSEZn7g5qMdK1k5HxNcg1NoXRO2CxMrX4Xl5kbFDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T16:10:38.665505Z"},"content_sha256":"af5501d9f813b91a1e208987c1a4b649b42dc26c064fee6219f3273288ddd0d5","schema_version":"1.0","event_id":"sha256:af5501d9f813b91a1e208987c1a4b649b42dc26c064fee6219f3273288ddd0d5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WZHGXU5IFJKPQ4FLLWR2I2JXW4/bundle.json","state_url":"https://pith.science/pith/WZHGXU5IFJKPQ4FLLWR2I2JXW4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WZHGXU5IFJKPQ4FLLWR2I2JXW4/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":"2026-08-17T16:10:38Z","links":{"resolver":"https://pith.science/pith/WZHGXU5IFJKPQ4FLLWR2I2JXW4","bundle":"https://pith.science/pith/WZHGXU5IFJKPQ4FLLWR2I2JXW4/bundle.json","state":"https://pith.science/pith/WZHGXU5IFJKPQ4FLLWR2I2JXW4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WZHGXU5IFJKPQ4FLLWR2I2JXW4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:WZHGXU5IFJKPQ4FLLWR2I2JXW4","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":"ee3a6a69b767afbe9fa73c3fdadae0d459e6cd16d775f6326065108dfd7a4478","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-11-06T16:04:48Z","title_canon_sha256":"5246233b3a6011939d97aac4e2b862867b813f17e1694a56562a1e9c5b68007b"},"schema_version":"1.0","source":{"id":"1611.01799","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.01799","created_at":"2026-05-18T01:00:04Z"},{"alias_kind":"arxiv_version","alias_value":"1611.01799v1","created_at":"2026-05-18T01:00:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.01799","created_at":"2026-05-18T01:00:04Z"},{"alias_kind":"pith_short_12","alias_value":"WZHGXU5IFJKP","created_at":"2026-05-18T12:30:51Z"},{"alias_kind":"pith_short_16","alias_value":"WZHGXU5IFJKPQ4FL","created_at":"2026-05-18T12:30:51Z"},{"alias_kind":"pith_short_8","alias_value":"WZHGXU5I","created_at":"2026-05-18T12:30:51Z"}],"graph_snapshots":[{"event_id":"sha256:af5501d9f813b91a1e208987c1a4b649b42dc26c064fee6219f3273288ddd0d5","target":"graph","created_at":"2026-05-18T01:00:04Z","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":"In this paper, we study deep generative models for effective unsupervised learning. We propose VGAN, which works by minimizing a variational lower bound of the negative log likelihood (NLL) of an energy based model (EBM), where the model density $p(\\mathbf{x})$ is approximated by a variational distribution $q(\\mathbf{x})$ that is easy to sample from. The training of VGAN takes a two step procedure: given $p(\\mathbf{x})$, $q(\\mathbf{x})$ is updated to maximize the lower bound; $p(\\mathbf{x})$ is then updated one step with samples drawn from $q(\\mathbf{x})$ to decrease the lower bound. VGAN is i","authors_text":"Rogerio Feris, Shuangfei Zhai, Yu Cheng, Zhongfei Zhang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-11-06T16:04:48Z","title":"Generative Adversarial Networks as Variational Training of Energy Based Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.01799","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":"sha256:116792a118386ad26b17397d1aa19aa08b6ce5d9d61439b475238ecf246a1c02","target":"record","created_at":"2026-05-18T01:00:04Z","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":"ee3a6a69b767afbe9fa73c3fdadae0d459e6cd16d775f6326065108dfd7a4478","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-11-06T16:04:48Z","title_canon_sha256":"5246233b3a6011939d97aac4e2b862867b813f17e1694a56562a1e9c5b68007b"},"schema_version":"1.0","source":{"id":"1611.01799","kind":"arxiv","version":1}},"canonical_sha256":"b64e6bd3a82a54f870ab5da3a46937b714e3c9c601b177bb798dde3ab67ab42c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b64e6bd3a82a54f870ab5da3a46937b714e3c9c601b177bb798dde3ab67ab42c","first_computed_at":"2026-05-18T01:00:04.459343Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:00:04.459343Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SZ2KbrkQ9DbztWyQwQEfJBT1iAPXie2WYohThq5yhn1dMzZxN9gn2NDXrcgY8aossUO1OHdK2JsOTRxppbJRAA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:00:04.459950Z","signed_message":"canonical_sha256_bytes"},"source_id":"1611.01799","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:116792a118386ad26b17397d1aa19aa08b6ce5d9d61439b475238ecf246a1c02","sha256:af5501d9f813b91a1e208987c1a4b649b42dc26c064fee6219f3273288ddd0d5"],"state_sha256":"9dd9bb4c1ff88bf155f329e3a58e7efd77701becf7258c13521b02c49be8f36f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4PiNzfD6bGTAJPj8tbTAaSaozUj0ffKn3VXLHaZ7L64Q+zpg2aRveFouyKmSpboHzlPJtCnn9tFdjFsYQwVPBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T16:10:38.670104Z","bundle_sha256":"2a05a6a925190308f36d145ce4b4b3a7d27e4e36807ad47742b84592e7f39554"}}