{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:I46LOTAZ3Q4MN5XCSG2JS327LL","short_pith_number":"pith:I46LOTAZ","canonical_record":{"source":{"id":"1807.04106","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-11T12:38:30Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"8407db2726c81df95f6341fff00ff80a780463f60e2e9f2b63a6e4c98c9a5e3a","abstract_canon_sha256":"7c6a6d50b9a41a56624cf9cf679a1591db6c4c43c18730f7607ebd00d7394547"},"schema_version":"1.0"},"canonical_sha256":"473cb74c19dc38c6f6e291b4996f5f5ae41176e4154a79ba8700c49e3f4fd1d8","source":{"kind":"arxiv","id":"1807.04106","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.04106","created_at":"2026-05-18T00:10:57Z"},{"alias_kind":"arxiv_version","alias_value":"1807.04106v1","created_at":"2026-05-18T00:10:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.04106","created_at":"2026-05-18T00:10:57Z"},{"alias_kind":"pith_short_12","alias_value":"I46LOTAZ3Q4M","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_16","alias_value":"I46LOTAZ3Q4MN5XC","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_8","alias_value":"I46LOTAZ","created_at":"2026-05-18T12:32:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:I46LOTAZ3Q4MN5XCSG2JS327LL","target":"record","payload":{"canonical_record":{"source":{"id":"1807.04106","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-11T12:38:30Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"8407db2726c81df95f6341fff00ff80a780463f60e2e9f2b63a6e4c98c9a5e3a","abstract_canon_sha256":"7c6a6d50b9a41a56624cf9cf679a1591db6c4c43c18730f7607ebd00d7394547"},"schema_version":"1.0"},"canonical_sha256":"473cb74c19dc38c6f6e291b4996f5f5ae41176e4154a79ba8700c49e3f4fd1d8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:10:57.965591Z","signature_b64":"HgvcR5h+bFPhBAxbSzq5b1kog+SiXiXCpSQkdJKTHBLdGuZez0MQBQ7HvCvGtWSlqjfSoQkDaSf0nJHEY4+XCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"473cb74c19dc38c6f6e291b4996f5f5ae41176e4154a79ba8700c49e3f4fd1d8","last_reissued_at":"2026-05-18T00:10:57.964824Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:10:57.964824Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.04106","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":"2026-05-18T00:10:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Oz0l1b+vbgKw5vUCRnK4WXMX/vJuk/rGu3fr6w2QoP6BD6v0ypoo/bxwYazEohOTlRYjnORvlSAnd4bh8AnMCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T06:31:37.887213Z"},"content_sha256":"96cd0dc4cce7ccd760e10e264b6eb138d2df9c53a18cbdc039ab23dbcef1ee77","schema_version":"1.0","event_id":"sha256:96cd0dc4cce7ccd760e10e264b6eb138d2df9c53a18cbdc039ab23dbcef1ee77"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:I46LOTAZ3Q4MN5XCSG2JS327LL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"VFunc: a Deep Generative Model for Functions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Alessandro Sordoni, Philip Bachman, Riashat Islam, Zafarali Ahmed","submitted_at":"2018-07-11T12:38:30Z","abstract_excerpt":"We introduce a deep generative model for functions. Our model provides a joint distribution p(f, z) over functions f and latent variables z which lets us efficiently sample from the marginal p(f) and maximize a variational lower bound on the entropy H(f). We can thus maximize objectives of the form E_{f~p(f)}[R(f)] + c*H(f), where R(f) denotes, e.g., a data log-likelihood term or an expected reward. Such objectives encompass Bayesian deep learning in function space, rather than parameter space, and Bayesian deep RL with representations of uncertainty that offer benefits over bootstrapping and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.04106","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":"2026-05-18T00:10:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Va/b8G01KWsDN87S6aAxs603ms6Ldau+EJxep8ogONtNpudnuC23Xv6KClIEL9kLJ/soFrUNq4TSuEA3kRVaBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T06:31:37.887943Z"},"content_sha256":"d2382ca54b48ce93c1c27078331898e02495d1b84606a9d8e1389b2a83919f37","schema_version":"1.0","event_id":"sha256:d2382ca54b48ce93c1c27078331898e02495d1b84606a9d8e1389b2a83919f37"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/I46LOTAZ3Q4MN5XCSG2JS327LL/bundle.json","state_url":"https://pith.science/pith/I46LOTAZ3Q4MN5XCSG2JS327LL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/I46LOTAZ3Q4MN5XCSG2JS327LL/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":"2026-05-25T06:31:37Z","links":{"resolver":"https://pith.science/pith/I46LOTAZ3Q4MN5XCSG2JS327LL","bundle":"https://pith.science/pith/I46LOTAZ3Q4MN5XCSG2JS327LL/bundle.json","state":"https://pith.science/pith/I46LOTAZ3Q4MN5XCSG2JS327LL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/I46LOTAZ3Q4MN5XCSG2JS327LL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:I46LOTAZ3Q4MN5XCSG2JS327LL","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":"7c6a6d50b9a41a56624cf9cf679a1591db6c4c43c18730f7607ebd00d7394547","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-11T12:38:30Z","title_canon_sha256":"8407db2726c81df95f6341fff00ff80a780463f60e2e9f2b63a6e4c98c9a5e3a"},"schema_version":"1.0","source":{"id":"1807.04106","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.04106","created_at":"2026-05-18T00:10:57Z"},{"alias_kind":"arxiv_version","alias_value":"1807.04106v1","created_at":"2026-05-18T00:10:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.04106","created_at":"2026-05-18T00:10:57Z"},{"alias_kind":"pith_short_12","alias_value":"I46LOTAZ3Q4M","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_16","alias_value":"I46LOTAZ3Q4MN5XC","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_8","alias_value":"I46LOTAZ","created_at":"2026-05-18T12:32:28Z"}],"graph_snapshots":[{"event_id":"sha256:d2382ca54b48ce93c1c27078331898e02495d1b84606a9d8e1389b2a83919f37","target":"graph","created_at":"2026-05-18T00:10:57Z","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 introduce a deep generative model for functions. Our model provides a joint distribution p(f, z) over functions f and latent variables z which lets us efficiently sample from the marginal p(f) and maximize a variational lower bound on the entropy H(f). We can thus maximize objectives of the form E_{f~p(f)}[R(f)] + c*H(f), where R(f) denotes, e.g., a data log-likelihood term or an expected reward. Such objectives encompass Bayesian deep learning in function space, rather than parameter space, and Bayesian deep RL with representations of uncertainty that offer benefits over bootstrapping and ","authors_text":"Alessandro Sordoni, Philip Bachman, Riashat Islam, Zafarali Ahmed","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-11T12:38:30Z","title":"VFunc: a Deep Generative Model for Functions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.04106","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":"sha256:96cd0dc4cce7ccd760e10e264b6eb138d2df9c53a18cbdc039ab23dbcef1ee77","target":"record","created_at":"2026-05-18T00:10:57Z","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":"7c6a6d50b9a41a56624cf9cf679a1591db6c4c43c18730f7607ebd00d7394547","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-11T12:38:30Z","title_canon_sha256":"8407db2726c81df95f6341fff00ff80a780463f60e2e9f2b63a6e4c98c9a5e3a"},"schema_version":"1.0","source":{"id":"1807.04106","kind":"arxiv","version":1}},"canonical_sha256":"473cb74c19dc38c6f6e291b4996f5f5ae41176e4154a79ba8700c49e3f4fd1d8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"473cb74c19dc38c6f6e291b4996f5f5ae41176e4154a79ba8700c49e3f4fd1d8","first_computed_at":"2026-05-18T00:10:57.964824Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:10:57.964824Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HgvcR5h+bFPhBAxbSzq5b1kog+SiXiXCpSQkdJKTHBLdGuZez0MQBQ7HvCvGtWSlqjfSoQkDaSf0nJHEY4+XCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:10:57.965591Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.04106","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:96cd0dc4cce7ccd760e10e264b6eb138d2df9c53a18cbdc039ab23dbcef1ee77","sha256:d2382ca54b48ce93c1c27078331898e02495d1b84606a9d8e1389b2a83919f37"],"state_sha256":"e43b67bfeee76c08e7f5430d45897da1c9c0e904d7803563528c278989d680ed"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"m4tCY6S8RrDF/60J65ZlvbqUnUr2380pTMn0o1ps7pRbMQv2mRa8tbUZoX2yiVN1Sj2IHITeJzYye9CZPzc6Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T06:31:37.891385Z","bundle_sha256":"9654bc796798ab83605b78234701d429fef6118d1598bc1fef8964e11e7c2f33"}}