{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:RCAPWOQAMSVIKKIN2TCW2KPZXI","short_pith_number":"pith:RCAPWOQA","canonical_record":{"source":{"id":"1507.01053","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2015-07-04T01:06:16Z","cross_cats_sorted":["cs.CL","cs.CV","cs.LG"],"title_canon_sha256":"0bf15d27a4c9557334295f20adb6e6c8876f99ca6496bcc27158a8cf2cd09480","abstract_canon_sha256":"6b592652295dd0c42a649282d4fd9866abd5f7d46c838cf6521cd1f786b6a798"},"schema_version":"1.0"},"canonical_sha256":"8880fb3a0064aa85290dd4c56d29f9ba0f9095032be6ce8065e196501c5d7136","source":{"kind":"arxiv","id":"1507.01053","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1507.01053","created_at":"2026-05-18T00:58:58Z"},{"alias_kind":"arxiv_version","alias_value":"1507.01053v1","created_at":"2026-05-18T00:58:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1507.01053","created_at":"2026-05-18T00:58:58Z"},{"alias_kind":"pith_short_12","alias_value":"RCAPWOQAMSVI","created_at":"2026-05-18T12:29:39Z"},{"alias_kind":"pith_short_16","alias_value":"RCAPWOQAMSVIKKIN","created_at":"2026-05-18T12:29:39Z"},{"alias_kind":"pith_short_8","alias_value":"RCAPWOQA","created_at":"2026-05-18T12:29:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:RCAPWOQAMSVIKKIN2TCW2KPZXI","target":"record","payload":{"canonical_record":{"source":{"id":"1507.01053","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2015-07-04T01:06:16Z","cross_cats_sorted":["cs.CL","cs.CV","cs.LG"],"title_canon_sha256":"0bf15d27a4c9557334295f20adb6e6c8876f99ca6496bcc27158a8cf2cd09480","abstract_canon_sha256":"6b592652295dd0c42a649282d4fd9866abd5f7d46c838cf6521cd1f786b6a798"},"schema_version":"1.0"},"canonical_sha256":"8880fb3a0064aa85290dd4c56d29f9ba0f9095032be6ce8065e196501c5d7136","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:58:58.116472Z","signature_b64":"NQ0agKusengxlpkPOlLQw676I8okhhZFSeXBKE7nxkrV7tUPa0GNp30zesWKmA6MGpQQ0UG9ycUoTEidrSqqAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8880fb3a0064aa85290dd4c56d29f9ba0f9095032be6ce8065e196501c5d7136","last_reissued_at":"2026-05-18T00:58:58.115962Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:58:58.115962Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1507.01053","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":"2026-05-18T00:58:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DxnJAUoYhtqNM3iE5d1Le7NVr4/xT97Ql4/tLFDTqTSdluxFaymrwOqYzlRNyE+FtzPYjvabXoPLhkhxiBMaCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T10:16:10.907548Z"},"content_sha256":"c48385b80aa491f98fa1200642098adcac78ad9c7ce2f68a450538fef34927a1","schema_version":"1.0","event_id":"sha256:c48385b80aa491f98fa1200642098adcac78ad9c7ce2f68a450538fef34927a1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:RCAPWOQAMSVIKKIN2TCW2KPZXI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Describing Multimedia Content using Attention-based Encoder--Decoder Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.CV","cs.LG"],"primary_cat":"cs.NE","authors_text":"Aaron Courville, Kyunghyun Cho, Yoshua Bengio","submitted_at":"2015-07-04T01:06:16Z","abstract_excerpt":"Whereas deep neural networks were first mostly used for classification tasks, they are rapidly expanding in the realm of structured output problems, where the observed target is composed of multiple random variables that have a rich joint distribution, given the input. We focus in this paper on the case where the input also has a rich structure and the input and output structures are somehow related. We describe systems that learn to attend to different places in the input, for each element of the output, for a variety of tasks: machine translation, image caption generation, video clip descrip"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.01053","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":"2026-05-18T00:58:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TnVmWrsEJ8/BNoPPSqdrIzuRR/teqoo+nac0A9Y5UcUfgTos+4vC8rDA2rUQWhb8IM4bygcYYzt9Y2vS12LRAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T10:16:10.908129Z"},"content_sha256":"7bac16082d1cdfeb074e8c103e0c39a4a7d62e5e2d4534102de13f3125982d70","schema_version":"1.0","event_id":"sha256:7bac16082d1cdfeb074e8c103e0c39a4a7d62e5e2d4534102de13f3125982d70"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RCAPWOQAMSVIKKIN2TCW2KPZXI/bundle.json","state_url":"https://pith.science/pith/RCAPWOQAMSVIKKIN2TCW2KPZXI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RCAPWOQAMSVIKKIN2TCW2KPZXI/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":"2026-06-07T10:16:10Z","links":{"resolver":"https://pith.science/pith/RCAPWOQAMSVIKKIN2TCW2KPZXI","bundle":"https://pith.science/pith/RCAPWOQAMSVIKKIN2TCW2KPZXI/bundle.json","state":"https://pith.science/pith/RCAPWOQAMSVIKKIN2TCW2KPZXI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RCAPWOQAMSVIKKIN2TCW2KPZXI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:RCAPWOQAMSVIKKIN2TCW2KPZXI","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":"6b592652295dd0c42a649282d4fd9866abd5f7d46c838cf6521cd1f786b6a798","cross_cats_sorted":["cs.CL","cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2015-07-04T01:06:16Z","title_canon_sha256":"0bf15d27a4c9557334295f20adb6e6c8876f99ca6496bcc27158a8cf2cd09480"},"schema_version":"1.0","source":{"id":"1507.01053","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1507.01053","created_at":"2026-05-18T00:58:58Z"},{"alias_kind":"arxiv_version","alias_value":"1507.01053v1","created_at":"2026-05-18T00:58:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1507.01053","created_at":"2026-05-18T00:58:58Z"},{"alias_kind":"pith_short_12","alias_value":"RCAPWOQAMSVI","created_at":"2026-05-18T12:29:39Z"},{"alias_kind":"pith_short_16","alias_value":"RCAPWOQAMSVIKKIN","created_at":"2026-05-18T12:29:39Z"},{"alias_kind":"pith_short_8","alias_value":"RCAPWOQA","created_at":"2026-05-18T12:29:39Z"}],"graph_snapshots":[{"event_id":"sha256:7bac16082d1cdfeb074e8c103e0c39a4a7d62e5e2d4534102de13f3125982d70","target":"graph","created_at":"2026-05-18T00:58:58Z","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":"Whereas deep neural networks were first mostly used for classification tasks, they are rapidly expanding in the realm of structured output problems, where the observed target is composed of multiple random variables that have a rich joint distribution, given the input. We focus in this paper on the case where the input also has a rich structure and the input and output structures are somehow related. We describe systems that learn to attend to different places in the input, for each element of the output, for a variety of tasks: machine translation, image caption generation, video clip descrip","authors_text":"Aaron Courville, Kyunghyun Cho, Yoshua Bengio","cross_cats":["cs.CL","cs.CV","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2015-07-04T01:06:16Z","title":"Describing Multimedia Content using Attention-based Encoder--Decoder Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.01053","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:c48385b80aa491f98fa1200642098adcac78ad9c7ce2f68a450538fef34927a1","target":"record","created_at":"2026-05-18T00:58:58Z","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":"6b592652295dd0c42a649282d4fd9866abd5f7d46c838cf6521cd1f786b6a798","cross_cats_sorted":["cs.CL","cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2015-07-04T01:06:16Z","title_canon_sha256":"0bf15d27a4c9557334295f20adb6e6c8876f99ca6496bcc27158a8cf2cd09480"},"schema_version":"1.0","source":{"id":"1507.01053","kind":"arxiv","version":1}},"canonical_sha256":"8880fb3a0064aa85290dd4c56d29f9ba0f9095032be6ce8065e196501c5d7136","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8880fb3a0064aa85290dd4c56d29f9ba0f9095032be6ce8065e196501c5d7136","first_computed_at":"2026-05-18T00:58:58.115962Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:58:58.115962Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NQ0agKusengxlpkPOlLQw676I8okhhZFSeXBKE7nxkrV7tUPa0GNp30zesWKmA6MGpQQ0UG9ycUoTEidrSqqAw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:58:58.116472Z","signed_message":"canonical_sha256_bytes"},"source_id":"1507.01053","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c48385b80aa491f98fa1200642098adcac78ad9c7ce2f68a450538fef34927a1","sha256:7bac16082d1cdfeb074e8c103e0c39a4a7d62e5e2d4534102de13f3125982d70"],"state_sha256":"a977b86633124e73c2aaeebf1a6078dd16634c7efcd25d60c9b88c6367b3a406"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"euzF2H9iGsL2/QHvQDc4BQMjm+34/4HR6rqErjSmFzeBiKUr1ND0iBGRA1QZoIzIEn6Z1+xinSNW2O7yqBgiBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T10:16:10.911110Z","bundle_sha256":"42f73422fc10eef75825ff0ce5fc4109bad63922620916ff80acb4c5eff39067"}}