{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:BKF36V7J54B4JDOKROVC3M3HPL","short_pith_number":"pith:BKF36V7J","canonical_record":{"source":{"id":"1702.08396","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-02-27T17:43:34Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"a5ca47dbf4e8a5e25b95a2b3986e25d2b556b9f666f576729cbcf235415e98d4","abstract_canon_sha256":"844f1bf68a5672d03f9c849ac1a74592bbb924d1bf0a53fcba51924999cb655f"},"schema_version":"1.0"},"canonical_sha256":"0a8bbf57e9ef03c48dca8baa2db3677ad4075b859c594fb86fa7edb2e9614f41","source":{"kind":"arxiv","id":"1702.08396","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1702.08396","created_at":"2026-05-18T00:42:43Z"},{"alias_kind":"arxiv_version","alias_value":"1702.08396v2","created_at":"2026-05-18T00:42:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.08396","created_at":"2026-05-18T00:42:43Z"},{"alias_kind":"pith_short_12","alias_value":"BKF36V7J54B4","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_16","alias_value":"BKF36V7J54B4JDOK","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_8","alias_value":"BKF36V7J","created_at":"2026-05-18T12:31:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:BKF36V7J54B4JDOKROVC3M3HPL","target":"record","payload":{"canonical_record":{"source":{"id":"1702.08396","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-02-27T17:43:34Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"a5ca47dbf4e8a5e25b95a2b3986e25d2b556b9f666f576729cbcf235415e98d4","abstract_canon_sha256":"844f1bf68a5672d03f9c849ac1a74592bbb924d1bf0a53fcba51924999cb655f"},"schema_version":"1.0"},"canonical_sha256":"0a8bbf57e9ef03c48dca8baa2db3677ad4075b859c594fb86fa7edb2e9614f41","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:42:43.859890Z","signature_b64":"gs8YTJqwfgXw4U+siSCvHKHbyzuza4enGo8hR4lf0fNOtmvaQ8ZLeG6qrqKk0eUmrDB64C+bf3VyqX8AcuJjBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0a8bbf57e9ef03c48dca8baa2db3677ad4075b859c594fb86fa7edb2e9614f41","last_reissued_at":"2026-05-18T00:42:43.859270Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:42:43.859270Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1702.08396","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":"2026-05-18T00:42:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qH7mpu0sNntBkPdi0kqrSR39pbL7Z6BEH3tApAz28g2rJlStYQE173DquOcyoiUyfUkfEFy2gNttenvxHallAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T18:36:40.957086Z"},"content_sha256":"331f69ff1373ea269a3a631435aa998d242231caa85a223645e2963466fcc445","schema_version":"1.0","event_id":"sha256:331f69ff1373ea269a3a631435aa998d242231caa85a223645e2963466fcc445"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:BKF36V7J54B4JDOKROVC3M3HPL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Hierarchical Features from Generative Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Jiaming Song, Shengjia Zhao, Stefano Ermon","submitted_at":"2017-02-27T17:43:34Z","abstract_excerpt":"Deep neural networks have been shown to be very successful at learning feature hierarchies in supervised learning tasks. Generative models, on the other hand, have benefited less from hierarchical models with multiple layers of latent variables. In this paper, we prove that hierarchical latent variable models do not take advantage of the hierarchical structure when trained with existing variational methods, and provide some limitations on the kind of features existing models can learn. Finally we propose an alternative architecture that do not suffer from these limitations. Our model is able t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.08396","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":""},"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":"2026-05-18T00:42:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Z5tBmqLipGMUCR+oYZC5biDJXWRh60NZ3cBPzIUGKGNLgW00vEtsITpAFVM5iuGPOMCXcVAwkL11YVxJtFEBAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T18:36:40.957446Z"},"content_sha256":"9ff1b91853d073355a3a1171d26cde0a01dc42bad4b08576cc5750e5f6534a61","schema_version":"1.0","event_id":"sha256:9ff1b91853d073355a3a1171d26cde0a01dc42bad4b08576cc5750e5f6534a61"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BKF36V7J54B4JDOKROVC3M3HPL/bundle.json","state_url":"https://pith.science/pith/BKF36V7J54B4JDOKROVC3M3HPL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BKF36V7J54B4JDOKROVC3M3HPL/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":"2026-06-02T18:36:40Z","links":{"resolver":"https://pith.science/pith/BKF36V7J54B4JDOKROVC3M3HPL","bundle":"https://pith.science/pith/BKF36V7J54B4JDOKROVC3M3HPL/bundle.json","state":"https://pith.science/pith/BKF36V7J54B4JDOKROVC3M3HPL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BKF36V7J54B4JDOKROVC3M3HPL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:BKF36V7J54B4JDOKROVC3M3HPL","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":"844f1bf68a5672d03f9c849ac1a74592bbb924d1bf0a53fcba51924999cb655f","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-02-27T17:43:34Z","title_canon_sha256":"a5ca47dbf4e8a5e25b95a2b3986e25d2b556b9f666f576729cbcf235415e98d4"},"schema_version":"1.0","source":{"id":"1702.08396","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1702.08396","created_at":"2026-05-18T00:42:43Z"},{"alias_kind":"arxiv_version","alias_value":"1702.08396v2","created_at":"2026-05-18T00:42:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.08396","created_at":"2026-05-18T00:42:43Z"},{"alias_kind":"pith_short_12","alias_value":"BKF36V7J54B4","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_16","alias_value":"BKF36V7J54B4JDOK","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_8","alias_value":"BKF36V7J","created_at":"2026-05-18T12:31:08Z"}],"graph_snapshots":[{"event_id":"sha256:9ff1b91853d073355a3a1171d26cde0a01dc42bad4b08576cc5750e5f6534a61","target":"graph","created_at":"2026-05-18T00:42:43Z","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":"Deep neural networks have been shown to be very successful at learning feature hierarchies in supervised learning tasks. Generative models, on the other hand, have benefited less from hierarchical models with multiple layers of latent variables. In this paper, we prove that hierarchical latent variable models do not take advantage of the hierarchical structure when trained with existing variational methods, and provide some limitations on the kind of features existing models can learn. Finally we propose an alternative architecture that do not suffer from these limitations. Our model is able t","authors_text":"Jiaming Song, Shengjia Zhao, Stefano Ermon","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-02-27T17:43:34Z","title":"Learning Hierarchical Features from Generative Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.08396","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":"sha256:331f69ff1373ea269a3a631435aa998d242231caa85a223645e2963466fcc445","target":"record","created_at":"2026-05-18T00:42:43Z","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":"844f1bf68a5672d03f9c849ac1a74592bbb924d1bf0a53fcba51924999cb655f","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-02-27T17:43:34Z","title_canon_sha256":"a5ca47dbf4e8a5e25b95a2b3986e25d2b556b9f666f576729cbcf235415e98d4"},"schema_version":"1.0","source":{"id":"1702.08396","kind":"arxiv","version":2}},"canonical_sha256":"0a8bbf57e9ef03c48dca8baa2db3677ad4075b859c594fb86fa7edb2e9614f41","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0a8bbf57e9ef03c48dca8baa2db3677ad4075b859c594fb86fa7edb2e9614f41","first_computed_at":"2026-05-18T00:42:43.859270Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:42:43.859270Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gs8YTJqwfgXw4U+siSCvHKHbyzuza4enGo8hR4lf0fNOtmvaQ8ZLeG6qrqKk0eUmrDB64C+bf3VyqX8AcuJjBA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:42:43.859890Z","signed_message":"canonical_sha256_bytes"},"source_id":"1702.08396","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:331f69ff1373ea269a3a631435aa998d242231caa85a223645e2963466fcc445","sha256:9ff1b91853d073355a3a1171d26cde0a01dc42bad4b08576cc5750e5f6534a61"],"state_sha256":"dafb434db7b72ff61204a705ee87f351a87fd27ae7eb950ec4b659dd1596a328"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fDl8Y7hAUn3SG7bsnNLB1yhCQEKIL7+O5s5xd59eoUGpNIpj8rhj3iauCDV36AiB127dd60hIJczvbHIiKZ4DQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T18:36:40.959327Z","bundle_sha256":"418dafe2128ee3fcd9b998084f338c65e4f32f7e6c1b700938f3889959cf10e8"}}