{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:WEKUUULASSUXBTZTOOHRRCRQ2X","short_pith_number":"pith:WEKUUULA","canonical_record":{"source":{"id":"1611.02266","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-11-07T20:57:24Z","cross_cats_sorted":["cs.AI","cs.CL","cs.LG"],"title_canon_sha256":"52dfd91ec7b8e1303a63c767ea3effeafab8cedf3f5d5202a43069513d4b21b0","abstract_canon_sha256":"c1d145a5646c25cf63cdbbf0c3e3c042b4d66200c607e9a5c999291948b1a2fb"},"schema_version":"1.0"},"canonical_sha256":"b1154a516094a970cf33738f188a30d5e28462026808e85e6b8ddc2151429766","source":{"kind":"arxiv","id":"1611.02266","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.02266","created_at":"2026-05-18T00:56:11Z"},{"alias_kind":"arxiv_version","alias_value":"1611.02266v2","created_at":"2026-05-18T00:56:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.02266","created_at":"2026-05-18T00:56:11Z"},{"alias_kind":"pith_short_12","alias_value":"WEKUUULASSUX","created_at":"2026-05-18T12:30:48Z"},{"alias_kind":"pith_short_16","alias_value":"WEKUUULASSUXBTZT","created_at":"2026-05-18T12:30:48Z"},{"alias_kind":"pith_short_8","alias_value":"WEKUUULA","created_at":"2026-05-18T12:30:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:WEKUUULASSUXBTZTOOHRRCRQ2X","target":"record","payload":{"canonical_record":{"source":{"id":"1611.02266","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-11-07T20:57:24Z","cross_cats_sorted":["cs.AI","cs.CL","cs.LG"],"title_canon_sha256":"52dfd91ec7b8e1303a63c767ea3effeafab8cedf3f5d5202a43069513d4b21b0","abstract_canon_sha256":"c1d145a5646c25cf63cdbbf0c3e3c042b4d66200c607e9a5c999291948b1a2fb"},"schema_version":"1.0"},"canonical_sha256":"b1154a516094a970cf33738f188a30d5e28462026808e85e6b8ddc2151429766","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:56:11.807161Z","signature_b64":"1cfLn6Mw0RcOjLXGzTyUjXog8hOHtg8aHE32cLlYwWm4Z9Nw9lNQWq73vLVaaIe+FWSa97i2y/oKs5aRdqSmCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b1154a516094a970cf33738f188a30d5e28462026808e85e6b8ddc2151429766","last_reissued_at":"2026-05-18T00:56:11.806648Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:56:11.806648Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1611.02266","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":"2026-05-18T00:56:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nASiarnK4kKqe5BPLMR/deYWGzvCosDany8Uwjj726MFiOqc8NL6ssONqriNlHIUbbr0SQfvUkgIdlJvaHCWDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T19:57:17.201512Z"},"content_sha256":"9a3ced3a5e2e473d31bdb75d635c437729453b33f1e3e72d8db774538f0ac218","schema_version":"1.0","event_id":"sha256:9a3ced3a5e2e473d31bdb75d635c437729453b33f1e3e72d8db774538f0ac218"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:WEKUUULASSUXBTZTOOHRRCRQ2X","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.LG"],"primary_cat":"stat.ML","authors_text":"John Winn, Liwen Zhang, Ryota Tomioka","submitted_at":"2016-11-07T20:57:24Z","abstract_excerpt":"We propose the Gaussian attention model for content-based neural memory access. With the proposed attention model, a neural network has the additional degree of freedom to control the focus of its attention from a laser sharp attention to a broad attention. It is applicable whenever we can assume that the distance in the latent space reflects some notion of semantics. We use the proposed attention model as a scoring function for the embedding of a knowledge base into a continuous vector space and then train a model that performs question answering about the entities in the knowledge base. The "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.02266","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":"2026-05-18T00:56:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PLKLNH7i48h6DhCZP64G3WgHbjHtlPHxqPwfHNGBHmGoM7gGq0gzFZ+MEbplqhj/BzGPMgZaHtgn7QCU8+7pDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T19:57:17.202057Z"},"content_sha256":"8225a1daff6ac1c4a5455e3ad1485b4ca27dfb7477c46a1d7b901d291e02b170","schema_version":"1.0","event_id":"sha256:8225a1daff6ac1c4a5455e3ad1485b4ca27dfb7477c46a1d7b901d291e02b170"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WEKUUULASSUXBTZTOOHRRCRQ2X/bundle.json","state_url":"https://pith.science/pith/WEKUUULASSUXBTZTOOHRRCRQ2X/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WEKUUULASSUXBTZTOOHRRCRQ2X/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":"2026-06-03T19:57:17Z","links":{"resolver":"https://pith.science/pith/WEKUUULASSUXBTZTOOHRRCRQ2X","bundle":"https://pith.science/pith/WEKUUULASSUXBTZTOOHRRCRQ2X/bundle.json","state":"https://pith.science/pith/WEKUUULASSUXBTZTOOHRRCRQ2X/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WEKUUULASSUXBTZTOOHRRCRQ2X/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:WEKUUULASSUXBTZTOOHRRCRQ2X","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":"c1d145a5646c25cf63cdbbf0c3e3c042b4d66200c607e9a5c999291948b1a2fb","cross_cats_sorted":["cs.AI","cs.CL","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-11-07T20:57:24Z","title_canon_sha256":"52dfd91ec7b8e1303a63c767ea3effeafab8cedf3f5d5202a43069513d4b21b0"},"schema_version":"1.0","source":{"id":"1611.02266","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.02266","created_at":"2026-05-18T00:56:11Z"},{"alias_kind":"arxiv_version","alias_value":"1611.02266v2","created_at":"2026-05-18T00:56:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.02266","created_at":"2026-05-18T00:56:11Z"},{"alias_kind":"pith_short_12","alias_value":"WEKUUULASSUX","created_at":"2026-05-18T12:30:48Z"},{"alias_kind":"pith_short_16","alias_value":"WEKUUULASSUXBTZT","created_at":"2026-05-18T12:30:48Z"},{"alias_kind":"pith_short_8","alias_value":"WEKUUULA","created_at":"2026-05-18T12:30:48Z"}],"graph_snapshots":[{"event_id":"sha256:8225a1daff6ac1c4a5455e3ad1485b4ca27dfb7477c46a1d7b901d291e02b170","target":"graph","created_at":"2026-05-18T00:56:11Z","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 propose the Gaussian attention model for content-based neural memory access. With the proposed attention model, a neural network has the additional degree of freedom to control the focus of its attention from a laser sharp attention to a broad attention. It is applicable whenever we can assume that the distance in the latent space reflects some notion of semantics. We use the proposed attention model as a scoring function for the embedding of a knowledge base into a continuous vector space and then train a model that performs question answering about the entities in the knowledge base. The ","authors_text":"John Winn, Liwen Zhang, Ryota Tomioka","cross_cats":["cs.AI","cs.CL","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-11-07T20:57:24Z","title":"Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.02266","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":"sha256:9a3ced3a5e2e473d31bdb75d635c437729453b33f1e3e72d8db774538f0ac218","target":"record","created_at":"2026-05-18T00:56:11Z","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":"c1d145a5646c25cf63cdbbf0c3e3c042b4d66200c607e9a5c999291948b1a2fb","cross_cats_sorted":["cs.AI","cs.CL","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-11-07T20:57:24Z","title_canon_sha256":"52dfd91ec7b8e1303a63c767ea3effeafab8cedf3f5d5202a43069513d4b21b0"},"schema_version":"1.0","source":{"id":"1611.02266","kind":"arxiv","version":2}},"canonical_sha256":"b1154a516094a970cf33738f188a30d5e28462026808e85e6b8ddc2151429766","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b1154a516094a970cf33738f188a30d5e28462026808e85e6b8ddc2151429766","first_computed_at":"2026-05-18T00:56:11.806648Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:56:11.806648Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1cfLn6Mw0RcOjLXGzTyUjXog8hOHtg8aHE32cLlYwWm4Z9Nw9lNQWq73vLVaaIe+FWSa97i2y/oKs5aRdqSmCw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:56:11.807161Z","signed_message":"canonical_sha256_bytes"},"source_id":"1611.02266","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9a3ced3a5e2e473d31bdb75d635c437729453b33f1e3e72d8db774538f0ac218","sha256:8225a1daff6ac1c4a5455e3ad1485b4ca27dfb7477c46a1d7b901d291e02b170"],"state_sha256":"a191162593fcdc9ce602d2dce4d7249ca2fb853978b66d5d0e38ff38145a73c8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"X3KcXAUlqOkGU2xF3VghA4zH4llG36Eu+4j7A5j6/GVEexszwKHJwV2U4OwZr68pwfg7NpgmstodyZKFe8hBDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T19:57:17.204773Z","bundle_sha256":"2a66070a3ced9ab746adfd8728347f66c348769d09c3cb35cda9a256880c9274"}}