{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:VX2U2HR4YP5THNDBVVSNQGXY3P","short_pith_number":"pith:VX2U2HR4","canonical_record":{"source":{"id":"1708.00805","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-08-02T15:55:40Z","cross_cats_sorted":[],"title_canon_sha256":"ec2bcf9cf37522b3c432e85685006010bdddf3f6f105fab90145109acb358eeb","abstract_canon_sha256":"c500c2f8f2b91ff120430232a92bf9bea8b7548c4c423704f7655429a8db6faf"},"schema_version":"1.0"},"canonical_sha256":"adf54d1e3cc3fb33b461ad64d81af8dbeac1edd18f7ccf32f00445948d1fde22","source":{"kind":"arxiv","id":"1708.00805","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.00805","created_at":"2026-05-18T00:38:44Z"},{"alias_kind":"arxiv_version","alias_value":"1708.00805v1","created_at":"2026-05-18T00:38:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.00805","created_at":"2026-05-18T00:38:44Z"},{"alias_kind":"pith_short_12","alias_value":"VX2U2HR4YP5T","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"VX2U2HR4YP5THNDB","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"VX2U2HR4","created_at":"2026-05-18T12:31:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:VX2U2HR4YP5THNDBVVSNQGXY3P","target":"record","payload":{"canonical_record":{"source":{"id":"1708.00805","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-08-02T15:55:40Z","cross_cats_sorted":[],"title_canon_sha256":"ec2bcf9cf37522b3c432e85685006010bdddf3f6f105fab90145109acb358eeb","abstract_canon_sha256":"c500c2f8f2b91ff120430232a92bf9bea8b7548c4c423704f7655429a8db6faf"},"schema_version":"1.0"},"canonical_sha256":"adf54d1e3cc3fb33b461ad64d81af8dbeac1edd18f7ccf32f00445948d1fde22","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:38:44.589942Z","signature_b64":"7jrWVX2FjZeQG/SiMD6UZ/cGCIJ5/DgqiznYFZfvqvuLZFMaCGdkhnxOr0JaJZQXk9PXE+MML57AWyOFQvAdCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"adf54d1e3cc3fb33b461ad64d81af8dbeac1edd18f7ccf32f00445948d1fde22","last_reissued_at":"2026-05-18T00:38:44.589201Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:38:44.589201Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1708.00805","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":"2026-05-18T00:38:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vKxVxv/m/qM2FUnIls04KWi3HLsMRIwW2yEKjhGChLgYOwWT3zMuAYeJfHtG+zQw4kqJJO4vPJ3wwdpP3qx8BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T21:43:12.009648Z"},"content_sha256":"02126fc6b8d906681fba562032023e7aa7d33b7cf95a3fe1c72426aa9e7ff63d","schema_version":"1.0","event_id":"sha256:02126fc6b8d906681fba562032023e7aa7d33b7cf95a3fe1c72426aa9e7ff63d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:VX2U2HR4YP5THNDBVVSNQGXY3P","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Variational Generative Stochastic Networks with Collaborative Shaping","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Doina Precup, Philip Bachman","submitted_at":"2017-08-02T15:55:40Z","abstract_excerpt":"We develop an approach to training generative models based on unrolling a variational auto-encoder into a Markov chain, and shaping the chain's trajectories using a technique inspired by recent work in Approximate Bayesian computation. We show that the global minimizer of the resulting objective is achieved when the generative model reproduces the target distribution. To allow finer control over the behavior of the models, we add a regularization term inspired by techniques used for regularizing certain types of policy search in reinforcement learning. We present empirical results on the MNIST"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.00805","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":"2026-05-18T00:38:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QqVJKsY5R/BhVAuy1YCWVfG+vfBGCP8KusmJ4HFRPNDCzkZ6nmYZtjChwPdL64V6yJDVPf4kwdbJJz7ZdQ2OCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T21:43:12.010049Z"},"content_sha256":"757f61448489054a704af1ae7831afa24ca53bd7307a7a39e9b700c8d68c655d","schema_version":"1.0","event_id":"sha256:757f61448489054a704af1ae7831afa24ca53bd7307a7a39e9b700c8d68c655d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VX2U2HR4YP5THNDBVVSNQGXY3P/bundle.json","state_url":"https://pith.science/pith/VX2U2HR4YP5THNDBVVSNQGXY3P/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VX2U2HR4YP5THNDBVVSNQGXY3P/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":"2026-05-25T21:43:12Z","links":{"resolver":"https://pith.science/pith/VX2U2HR4YP5THNDBVVSNQGXY3P","bundle":"https://pith.science/pith/VX2U2HR4YP5THNDBVVSNQGXY3P/bundle.json","state":"https://pith.science/pith/VX2U2HR4YP5THNDBVVSNQGXY3P/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VX2U2HR4YP5THNDBVVSNQGXY3P/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:VX2U2HR4YP5THNDBVVSNQGXY3P","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":"c500c2f8f2b91ff120430232a92bf9bea8b7548c4c423704f7655429a8db6faf","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-08-02T15:55:40Z","title_canon_sha256":"ec2bcf9cf37522b3c432e85685006010bdddf3f6f105fab90145109acb358eeb"},"schema_version":"1.0","source":{"id":"1708.00805","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.00805","created_at":"2026-05-18T00:38:44Z"},{"alias_kind":"arxiv_version","alias_value":"1708.00805v1","created_at":"2026-05-18T00:38:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.00805","created_at":"2026-05-18T00:38:44Z"},{"alias_kind":"pith_short_12","alias_value":"VX2U2HR4YP5T","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"VX2U2HR4YP5THNDB","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"VX2U2HR4","created_at":"2026-05-18T12:31:49Z"}],"graph_snapshots":[{"event_id":"sha256:757f61448489054a704af1ae7831afa24ca53bd7307a7a39e9b700c8d68c655d","target":"graph","created_at":"2026-05-18T00:38:44Z","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":"We develop an approach to training generative models based on unrolling a variational auto-encoder into a Markov chain, and shaping the chain's trajectories using a technique inspired by recent work in Approximate Bayesian computation. We show that the global minimizer of the resulting objective is achieved when the generative model reproduces the target distribution. To allow finer control over the behavior of the models, we add a regularization term inspired by techniques used for regularizing certain types of policy search in reinforcement learning. We present empirical results on the MNIST","authors_text":"Doina Precup, Philip Bachman","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-08-02T15:55:40Z","title":"Variational Generative Stochastic Networks with Collaborative Shaping"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.00805","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":"sha256:02126fc6b8d906681fba562032023e7aa7d33b7cf95a3fe1c72426aa9e7ff63d","target":"record","created_at":"2026-05-18T00:38:44Z","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":"c500c2f8f2b91ff120430232a92bf9bea8b7548c4c423704f7655429a8db6faf","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-08-02T15:55:40Z","title_canon_sha256":"ec2bcf9cf37522b3c432e85685006010bdddf3f6f105fab90145109acb358eeb"},"schema_version":"1.0","source":{"id":"1708.00805","kind":"arxiv","version":1}},"canonical_sha256":"adf54d1e3cc3fb33b461ad64d81af8dbeac1edd18f7ccf32f00445948d1fde22","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"adf54d1e3cc3fb33b461ad64d81af8dbeac1edd18f7ccf32f00445948d1fde22","first_computed_at":"2026-05-18T00:38:44.589201Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:38:44.589201Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7jrWVX2FjZeQG/SiMD6UZ/cGCIJ5/DgqiznYFZfvqvuLZFMaCGdkhnxOr0JaJZQXk9PXE+MML57AWyOFQvAdCA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:38:44.589942Z","signed_message":"canonical_sha256_bytes"},"source_id":"1708.00805","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:02126fc6b8d906681fba562032023e7aa7d33b7cf95a3fe1c72426aa9e7ff63d","sha256:757f61448489054a704af1ae7831afa24ca53bd7307a7a39e9b700c8d68c655d"],"state_sha256":"82b7b562e0aadfd2537f2e78973be48a2c4628537d6d39ff60974f84093cfb0d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cgXXp74EFhW6V0/HRBJOPubNxzNS7mULrAztANTwQc4wDIeeaNKekmdSv7pjX0q4YzyxjDjEtbeWpAumIHpxDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T21:43:12.012900Z","bundle_sha256":"b301ce8fb027a4d8ed916fa95e890b5b85d91174de534eb0cf4687ce48842e9a"}}