{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:7SOHPIZGEB53XJAB2AL6RVJPQU","short_pith_number":"pith:7SOHPIZG","canonical_record":{"source":{"id":"2104.00428","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-04-01T12:19:54Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"6a6ad74ed5b84ce018bacd3ca6b8edbf14a4d118a46990e83cf1d49e89b871cd","abstract_canon_sha256":"724ba878a558a64a0535876e1caf4ef36e2a1f95b5bf45a0d6b9a63f80c97782"},"schema_version":"1.0"},"canonical_sha256":"fc9c77a326207bbba401d017e8d52f850ed010d913fa0cfd794240e43ad39ebd","source":{"kind":"arxiv","id":"2104.00428","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2104.00428","created_at":"2026-07-05T03:25:52Z"},{"alias_kind":"arxiv_version","alias_value":"2104.00428v3","created_at":"2026-07-05T03:25:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.00428","created_at":"2026-07-05T03:25:52Z"},{"alias_kind":"pith_short_12","alias_value":"7SOHPIZGEB53","created_at":"2026-07-05T03:25:52Z"},{"alias_kind":"pith_short_16","alias_value":"7SOHPIZGEB53XJAB","created_at":"2026-07-05T03:25:52Z"},{"alias_kind":"pith_short_8","alias_value":"7SOHPIZG","created_at":"2026-07-05T03:25:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:7SOHPIZGEB53XJAB2AL6RVJPQU","target":"record","payload":{"canonical_record":{"source":{"id":"2104.00428","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-04-01T12:19:54Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"6a6ad74ed5b84ce018bacd3ca6b8edbf14a4d118a46990e83cf1d49e89b871cd","abstract_canon_sha256":"724ba878a558a64a0535876e1caf4ef36e2a1f95b5bf45a0d6b9a63f80c97782"},"schema_version":"1.0"},"canonical_sha256":"fc9c77a326207bbba401d017e8d52f850ed010d913fa0cfd794240e43ad39ebd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:25:52.402952Z","signature_b64":"HVe84dFm/coHuJ8IWBGLgidTJzqA1C97hetRHxg0lkmgoRtBR12OPgupNOvQ9ZipzBxq82mNJ83Am4A/7fA6Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fc9c77a326207bbba401d017e8d52f850ed010d913fa0cfd794240e43ad39ebd","last_reissued_at":"2026-07-05T03:25:52.402516Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:25:52.402516Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2104.00428","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":"2026-07-05T03:25:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wZ4qm8vB7WTasYK2aeYRbpNS9Gzx6VUiIzI9xmvdD161eH3xcjTkvZceiC0zRifIHcFqVFg+LOS5EGjgSC78BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T07:40:40.012942Z"},"content_sha256":"ccf1e740d1b9676ee8990eff609b3cd249600be11848e6f549df3762a2192305","schema_version":"1.0","event_id":"sha256:ccf1e740d1b9676ee8990eff609b3cd249600be11848e6f549df3762a2192305"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:7SOHPIZGEB53XJAB2AL6RVJPQU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Storchastic: A Framework for General Stochastic Automatic Differentiation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"stat.ML","authors_text":"Annette ten Teije, Emile van Krieken, Jakub M. Tomczak","submitted_at":"2021-04-01T12:19:54Z","abstract_excerpt":"Modelers use automatic differentiation (AD) of computation graphs to implement complex Deep Learning models without defining gradient computations. Stochastic AD extends AD to stochastic computation graphs with sampling steps, which arise when modelers handle the intractable expectations common in Reinforcement Learning and Variational Inference. However, current methods for stochastic AD are limited: They are either only applicable to continuous random variables and differentiable functions, or can only use simple but high variance score-function estimators. To overcome these limitations, we "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.00428","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2104.00428/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T03:25:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ofD2hYOnQK68OInId8FoV9cJSqZEE7eOBycaiDGDmHqml4wwHI7yxMzF7ykheLiWL/+EjDFgvFoXddw3zWzsAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T07:40:40.013307Z"},"content_sha256":"b56c3a801c0f2a6f08bcea507e0c4ed63c0b76907e000cfeb06e2625f44785b4","schema_version":"1.0","event_id":"sha256:b56c3a801c0f2a6f08bcea507e0c4ed63c0b76907e000cfeb06e2625f44785b4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7SOHPIZGEB53XJAB2AL6RVJPQU/bundle.json","state_url":"https://pith.science/pith/7SOHPIZGEB53XJAB2AL6RVJPQU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7SOHPIZGEB53XJAB2AL6RVJPQU/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":"2026-07-05T07:40:40Z","links":{"resolver":"https://pith.science/pith/7SOHPIZGEB53XJAB2AL6RVJPQU","bundle":"https://pith.science/pith/7SOHPIZGEB53XJAB2AL6RVJPQU/bundle.json","state":"https://pith.science/pith/7SOHPIZGEB53XJAB2AL6RVJPQU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7SOHPIZGEB53XJAB2AL6RVJPQU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:7SOHPIZGEB53XJAB2AL6RVJPQU","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":"724ba878a558a64a0535876e1caf4ef36e2a1f95b5bf45a0d6b9a63f80c97782","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-04-01T12:19:54Z","title_canon_sha256":"6a6ad74ed5b84ce018bacd3ca6b8edbf14a4d118a46990e83cf1d49e89b871cd"},"schema_version":"1.0","source":{"id":"2104.00428","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2104.00428","created_at":"2026-07-05T03:25:52Z"},{"alias_kind":"arxiv_version","alias_value":"2104.00428v3","created_at":"2026-07-05T03:25:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.00428","created_at":"2026-07-05T03:25:52Z"},{"alias_kind":"pith_short_12","alias_value":"7SOHPIZGEB53","created_at":"2026-07-05T03:25:52Z"},{"alias_kind":"pith_short_16","alias_value":"7SOHPIZGEB53XJAB","created_at":"2026-07-05T03:25:52Z"},{"alias_kind":"pith_short_8","alias_value":"7SOHPIZG","created_at":"2026-07-05T03:25:52Z"}],"graph_snapshots":[{"event_id":"sha256:b56c3a801c0f2a6f08bcea507e0c4ed63c0b76907e000cfeb06e2625f44785b4","target":"graph","created_at":"2026-07-05T03:25:52Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2104.00428/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Modelers use automatic differentiation (AD) of computation graphs to implement complex Deep Learning models without defining gradient computations. Stochastic AD extends AD to stochastic computation graphs with sampling steps, which arise when modelers handle the intractable expectations common in Reinforcement Learning and Variational Inference. However, current methods for stochastic AD are limited: They are either only applicable to continuous random variables and differentiable functions, or can only use simple but high variance score-function estimators. To overcome these limitations, we ","authors_text":"Annette ten Teije, Emile van Krieken, Jakub M. Tomczak","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-04-01T12:19:54Z","title":"Storchastic: A Framework for General Stochastic Automatic Differentiation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.00428","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:ccf1e740d1b9676ee8990eff609b3cd249600be11848e6f549df3762a2192305","target":"record","created_at":"2026-07-05T03:25:52Z","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":"724ba878a558a64a0535876e1caf4ef36e2a1f95b5bf45a0d6b9a63f80c97782","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-04-01T12:19:54Z","title_canon_sha256":"6a6ad74ed5b84ce018bacd3ca6b8edbf14a4d118a46990e83cf1d49e89b871cd"},"schema_version":"1.0","source":{"id":"2104.00428","kind":"arxiv","version":3}},"canonical_sha256":"fc9c77a326207bbba401d017e8d52f850ed010d913fa0cfd794240e43ad39ebd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fc9c77a326207bbba401d017e8d52f850ed010d913fa0cfd794240e43ad39ebd","first_computed_at":"2026-07-05T03:25:52.402516Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:25:52.402516Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HVe84dFm/coHuJ8IWBGLgidTJzqA1C97hetRHxg0lkmgoRtBR12OPgupNOvQ9ZipzBxq82mNJ83Am4A/7fA6Aw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:25:52.402952Z","signed_message":"canonical_sha256_bytes"},"source_id":"2104.00428","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ccf1e740d1b9676ee8990eff609b3cd249600be11848e6f549df3762a2192305","sha256:b56c3a801c0f2a6f08bcea507e0c4ed63c0b76907e000cfeb06e2625f44785b4"],"state_sha256":"9d614ba95a058c75b5067f45acd19e349b53321367ea05b4a6a1e781e31bc7c8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+qJdt2vg9mh5EwCHc5M/ASxVa5U5+1vwVygxkFnO1G0w57Yp5HmoU0einj2kUVcgv0+X4hNa/IQZtCKP0lK2AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-05T07:40:40.015238Z","bundle_sha256":"1b06e6afe2eddb9080f8ad599cf8fa1bcfc3b943fab525e4fcc109bde993718f"}}