{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:LBGBTFNJKE6WPUZMHPSU5TGH4G","short_pith_number":"pith:LBGBTFNJ","canonical_record":{"source":{"id":"1702.02181","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-02-07T19:59:43Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"57c3308c4650398f2b70a96e7a801e5db7e408f75c7d1c9a83abeab3c601bf2a","abstract_canon_sha256":"9a4e0a024dc3bdfce4281bcce34793977c81f0937a94455d46d69562b7f8844e"},"schema_version":"1.0"},"canonical_sha256":"584c1995a9513d67d32c3be54eccc7e1ab058751494cbe307891070e001e8308","source":{"kind":"arxiv","id":"1702.02181","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1702.02181","created_at":"2026-05-18T00:50:13Z"},{"alias_kind":"arxiv_version","alias_value":"1702.02181v2","created_at":"2026-05-18T00:50:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.02181","created_at":"2026-05-18T00:50:13Z"},{"alias_kind":"pith_short_12","alias_value":"LBGBTFNJKE6W","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_16","alias_value":"LBGBTFNJKE6WPUZM","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_8","alias_value":"LBGBTFNJ","created_at":"2026-05-18T12:31:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:LBGBTFNJKE6WPUZMHPSU5TGH4G","target":"record","payload":{"canonical_record":{"source":{"id":"1702.02181","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-02-07T19:59:43Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"57c3308c4650398f2b70a96e7a801e5db7e408f75c7d1c9a83abeab3c601bf2a","abstract_canon_sha256":"9a4e0a024dc3bdfce4281bcce34793977c81f0937a94455d46d69562b7f8844e"},"schema_version":"1.0"},"canonical_sha256":"584c1995a9513d67d32c3be54eccc7e1ab058751494cbe307891070e001e8308","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:50:13.352996Z","signature_b64":"EAzVxAAgiSDZDJOYML1wWocFs7Bps+vUX+gikESaS6YLhijYdy9slnLsO72wCfXNWduhNCC1mqxSFIwS9Gh2BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"584c1995a9513d67d32c3be54eccc7e1ab058751494cbe307891070e001e8308","last_reissued_at":"2026-05-18T00:50:13.352396Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:50:13.352396Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1702.02181","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":"2026-05-18T00:50:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ADaewvXwogu190DWUSRCyOR5x+bYEonUmLR+KVAu8HiUYPUEE3/DZbaR8mUphV9xn2AQbesdBuDsGVM4B/HKCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T20:22:51.749628Z"},"content_sha256":"8bcd0423b1c58ec3622a8cb0b2efc068c838a321c1d026ebc533bb37212da318","schema_version":"1.0","event_id":"sha256:8bcd0423b1c58ec3622a8cb0b2efc068c838a321c1d026ebc533bb37212da318"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:LBGBTFNJKE6WPUZMHPSU5TGH4G","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Learning with Dynamic Computation Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.NE","authors_text":"DeLesley Hutchins, Marcello Herreshoff, Moshe Looks, Peter Norvig","submitted_at":"2017-02-07T19:59:43Z","abstract_excerpt":"Neural networks that compute over graph structures are a natural fit for problems in a variety of domains, including natural language (parse trees) and cheminformatics (molecular graphs). However, since the computation graph has a different shape and size for every input, such networks do not directly support batched training or inference. They are also difficult to implement in popular deep learning libraries, which are based on static data-flow graphs. We introduce a technique called dynamic batching, which not only batches together operations between different input graphs of dissimilar sha"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.02181","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":"2026-05-18T00:50:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nuW3EBT6+Fl4pCYJ1vZR3yWT/L3eyug0b7wUeDvIlCH0IU1b8wa/bKcvk4tZZZWi/H5GFfeqbjxlkaUX1SUvCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T20:22:51.749966Z"},"content_sha256":"cfce866c4cb9008c804fb517d77f7ab2b27ef0cac3afabefa25f5b00d68cba2c","schema_version":"1.0","event_id":"sha256:cfce866c4cb9008c804fb517d77f7ab2b27ef0cac3afabefa25f5b00d68cba2c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LBGBTFNJKE6WPUZMHPSU5TGH4G/bundle.json","state_url":"https://pith.science/pith/LBGBTFNJKE6WPUZMHPSU5TGH4G/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LBGBTFNJKE6WPUZMHPSU5TGH4G/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":"2026-06-03T20:22:51Z","links":{"resolver":"https://pith.science/pith/LBGBTFNJKE6WPUZMHPSU5TGH4G","bundle":"https://pith.science/pith/LBGBTFNJKE6WPUZMHPSU5TGH4G/bundle.json","state":"https://pith.science/pith/LBGBTFNJKE6WPUZMHPSU5TGH4G/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LBGBTFNJKE6WPUZMHPSU5TGH4G/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:LBGBTFNJKE6WPUZMHPSU5TGH4G","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":"9a4e0a024dc3bdfce4281bcce34793977c81f0937a94455d46d69562b7f8844e","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-02-07T19:59:43Z","title_canon_sha256":"57c3308c4650398f2b70a96e7a801e5db7e408f75c7d1c9a83abeab3c601bf2a"},"schema_version":"1.0","source":{"id":"1702.02181","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1702.02181","created_at":"2026-05-18T00:50:13Z"},{"alias_kind":"arxiv_version","alias_value":"1702.02181v2","created_at":"2026-05-18T00:50:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.02181","created_at":"2026-05-18T00:50:13Z"},{"alias_kind":"pith_short_12","alias_value":"LBGBTFNJKE6W","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_16","alias_value":"LBGBTFNJKE6WPUZM","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_8","alias_value":"LBGBTFNJ","created_at":"2026-05-18T12:31:28Z"}],"graph_snapshots":[{"event_id":"sha256:cfce866c4cb9008c804fb517d77f7ab2b27ef0cac3afabefa25f5b00d68cba2c","target":"graph","created_at":"2026-05-18T00:50:13Z","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":"Neural networks that compute over graph structures are a natural fit for problems in a variety of domains, including natural language (parse trees) and cheminformatics (molecular graphs). However, since the computation graph has a different shape and size for every input, such networks do not directly support batched training or inference. They are also difficult to implement in popular deep learning libraries, which are based on static data-flow graphs. We introduce a technique called dynamic batching, which not only batches together operations between different input graphs of dissimilar sha","authors_text":"DeLesley Hutchins, Marcello Herreshoff, Moshe Looks, Peter Norvig","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-02-07T19:59:43Z","title":"Deep Learning with Dynamic Computation Graphs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.02181","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":"sha256:8bcd0423b1c58ec3622a8cb0b2efc068c838a321c1d026ebc533bb37212da318","target":"record","created_at":"2026-05-18T00:50:13Z","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":"9a4e0a024dc3bdfce4281bcce34793977c81f0937a94455d46d69562b7f8844e","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-02-07T19:59:43Z","title_canon_sha256":"57c3308c4650398f2b70a96e7a801e5db7e408f75c7d1c9a83abeab3c601bf2a"},"schema_version":"1.0","source":{"id":"1702.02181","kind":"arxiv","version":2}},"canonical_sha256":"584c1995a9513d67d32c3be54eccc7e1ab058751494cbe307891070e001e8308","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"584c1995a9513d67d32c3be54eccc7e1ab058751494cbe307891070e001e8308","first_computed_at":"2026-05-18T00:50:13.352396Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:50:13.352396Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EAzVxAAgiSDZDJOYML1wWocFs7Bps+vUX+gikESaS6YLhijYdy9slnLsO72wCfXNWduhNCC1mqxSFIwS9Gh2BQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:50:13.352996Z","signed_message":"canonical_sha256_bytes"},"source_id":"1702.02181","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8bcd0423b1c58ec3622a8cb0b2efc068c838a321c1d026ebc533bb37212da318","sha256:cfce866c4cb9008c804fb517d77f7ab2b27ef0cac3afabefa25f5b00d68cba2c"],"state_sha256":"720987e683bd03c0faa2bbffda7edd836129d1d039e0f02f59d101645b45f0c0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OOSQJCXpyKgDdtzN2NavogN+Z/PJkLk3uIPK6EmwIDG7MxIyRzCeIrYYX45s0FCzcCZkgiyVZ12PcyeafGY8DA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T20:22:51.751861Z","bundle_sha256":"a9cfcbb84ecfdfa5991b074da326d9017843a6e6ce08f9e36ae1aa7bc6eaab8f"}}