{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:GMY4GG6W7BZI2PSUJBCQHV6PYH","short_pith_number":"pith:GMY4GG6W","canonical_record":{"source":{"id":"1802.02892","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-02-06T20:30:59Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"bedaf9d604440a1bafcfa4a44b18a4aa754e45feb560ffbb4f3238a4c1ef8558","abstract_canon_sha256":"38e91def78e626b9cb9bd165631e1a485b186a97f0db7b173debc48c597f0522"},"schema_version":"1.0"},"canonical_sha256":"3331c31bd6f8728d3e54484503d7cfc1d2b8ab2b468674cae54ce01b2a7bb3d8","source":{"kind":"arxiv","id":"1802.02892","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.02892","created_at":"2026-05-18T00:24:02Z"},{"alias_kind":"arxiv_version","alias_value":"1802.02892v1","created_at":"2026-05-18T00:24:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.02892","created_at":"2026-05-18T00:24:02Z"},{"alias_kind":"pith_short_12","alias_value":"GMY4GG6W7BZI","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"GMY4GG6W7BZI2PSU","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"GMY4GG6W","created_at":"2026-05-18T12:32:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:GMY4GG6W7BZI2PSUJBCQHV6PYH","target":"record","payload":{"canonical_record":{"source":{"id":"1802.02892","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-02-06T20:30:59Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"bedaf9d604440a1bafcfa4a44b18a4aa754e45feb560ffbb4f3238a4c1ef8558","abstract_canon_sha256":"38e91def78e626b9cb9bd165631e1a485b186a97f0db7b173debc48c597f0522"},"schema_version":"1.0"},"canonical_sha256":"3331c31bd6f8728d3e54484503d7cfc1d2b8ab2b468674cae54ce01b2a7bb3d8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:24:02.865840Z","signature_b64":"P2RMLRiCRlEBVlvm+zHzbml8M2nObVKdXNBEDenImv4BDcVmaYVC9GvOgFbXPTWi+sEDwJeHODMN/eNQaAeuDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3331c31bd6f8728d3e54484503d7cfc1d2b8ab2b468674cae54ce01b2a7bb3d8","last_reissued_at":"2026-05-18T00:24:02.865393Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:24:02.865393Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1802.02892","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":"2026-05-18T00:24:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yfkEhavnKRaCeb95ykeawz1eO9+ljZtTjjW3nx7FF4Tu4USPPQPwwmvD4D7IM7YA/EVl8vlbNVlsskg6AavrCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T20:04:40.379308Z"},"content_sha256":"eeda1655cf3439659918cb39f13e12621232c4daa1d8308df2c52c557026f929","schema_version":"1.0","event_id":"sha256:eeda1655cf3439659918cb39f13e12621232c4daa1d8308df2c52c557026f929"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:GMY4GG6W7BZI2PSUJBCQHV6PYH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient Large-Scale Multi-Modal Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.CL","authors_text":"A. Joulin, D. Kiela, E. Grave, T. Mikolov","submitted_at":"2018-02-06T20:30:59Z","abstract_excerpt":"While the incipient internet was largely text-based, the modern digital world is becoming increasingly multi-modal. Here, we examine multi-modal classification where one modality is discrete, e.g. text, and the other is continuous, e.g. visual representations transferred from a convolutional neural network. In particular, we focus on scenarios where we have to be able to classify large quantities of data quickly. We investigate various methods for performing multi-modal fusion and analyze their trade-offs in terms of classification accuracy and computational efficiency. Our findings indicate t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.02892","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":"2026-05-18T00:24:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Uo0Ceqz5iGnz2MNSTXlhQMRWnKaksZSMTxypqwO6/MDvS9D+STcaFSwYo4oObPz16vEkYnfL/6llg9pt01ECBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T20:04:40.380040Z"},"content_sha256":"2f65dd82f18a00409a60fa3bf91e183cd1b5f6b73eb4815e12599684f858e9be","schema_version":"1.0","event_id":"sha256:2f65dd82f18a00409a60fa3bf91e183cd1b5f6b73eb4815e12599684f858e9be"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GMY4GG6W7BZI2PSUJBCQHV6PYH/bundle.json","state_url":"https://pith.science/pith/GMY4GG6W7BZI2PSUJBCQHV6PYH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GMY4GG6W7BZI2PSUJBCQHV6PYH/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":"2026-06-05T20:04:40Z","links":{"resolver":"https://pith.science/pith/GMY4GG6W7BZI2PSUJBCQHV6PYH","bundle":"https://pith.science/pith/GMY4GG6W7BZI2PSUJBCQHV6PYH/bundle.json","state":"https://pith.science/pith/GMY4GG6W7BZI2PSUJBCQHV6PYH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GMY4GG6W7BZI2PSUJBCQHV6PYH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:GMY4GG6W7BZI2PSUJBCQHV6PYH","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":"38e91def78e626b9cb9bd165631e1a485b186a97f0db7b173debc48c597f0522","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-02-06T20:30:59Z","title_canon_sha256":"bedaf9d604440a1bafcfa4a44b18a4aa754e45feb560ffbb4f3238a4c1ef8558"},"schema_version":"1.0","source":{"id":"1802.02892","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.02892","created_at":"2026-05-18T00:24:02Z"},{"alias_kind":"arxiv_version","alias_value":"1802.02892v1","created_at":"2026-05-18T00:24:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.02892","created_at":"2026-05-18T00:24:02Z"},{"alias_kind":"pith_short_12","alias_value":"GMY4GG6W7BZI","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"GMY4GG6W7BZI2PSU","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"GMY4GG6W","created_at":"2026-05-18T12:32:25Z"}],"graph_snapshots":[{"event_id":"sha256:2f65dd82f18a00409a60fa3bf91e183cd1b5f6b73eb4815e12599684f858e9be","target":"graph","created_at":"2026-05-18T00:24:02Z","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":"While the incipient internet was largely text-based, the modern digital world is becoming increasingly multi-modal. Here, we examine multi-modal classification where one modality is discrete, e.g. text, and the other is continuous, e.g. visual representations transferred from a convolutional neural network. In particular, we focus on scenarios where we have to be able to classify large quantities of data quickly. We investigate various methods for performing multi-modal fusion and analyze their trade-offs in terms of classification accuracy and computational efficiency. Our findings indicate t","authors_text":"A. Joulin, D. Kiela, E. Grave, T. Mikolov","cross_cats":["cs.AI","cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-02-06T20:30:59Z","title":"Efficient Large-Scale Multi-Modal Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.02892","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:eeda1655cf3439659918cb39f13e12621232c4daa1d8308df2c52c557026f929","target":"record","created_at":"2026-05-18T00:24:02Z","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":"38e91def78e626b9cb9bd165631e1a485b186a97f0db7b173debc48c597f0522","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-02-06T20:30:59Z","title_canon_sha256":"bedaf9d604440a1bafcfa4a44b18a4aa754e45feb560ffbb4f3238a4c1ef8558"},"schema_version":"1.0","source":{"id":"1802.02892","kind":"arxiv","version":1}},"canonical_sha256":"3331c31bd6f8728d3e54484503d7cfc1d2b8ab2b468674cae54ce01b2a7bb3d8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3331c31bd6f8728d3e54484503d7cfc1d2b8ab2b468674cae54ce01b2a7bb3d8","first_computed_at":"2026-05-18T00:24:02.865393Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:24:02.865393Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"P2RMLRiCRlEBVlvm+zHzbml8M2nObVKdXNBEDenImv4BDcVmaYVC9GvOgFbXPTWi+sEDwJeHODMN/eNQaAeuDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:24:02.865840Z","signed_message":"canonical_sha256_bytes"},"source_id":"1802.02892","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eeda1655cf3439659918cb39f13e12621232c4daa1d8308df2c52c557026f929","sha256:2f65dd82f18a00409a60fa3bf91e183cd1b5f6b73eb4815e12599684f858e9be"],"state_sha256":"fbb40b840629cee51894493621951b24569a834a315189676763e29dde2eb252"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VzTAj69G1Csuw4qv2HjLB1rkomBl36nSTlaMe1VN7e4tpSiWNoyMRr1Gn84Uh4pSjTfKe3ivqEIxM/abWWsFCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T20:04:40.385681Z","bundle_sha256":"3c67fba55d3bbff9c0a213caf7766f1c63ac0020bcb0048b147aa1ebaa0ec559"}}