{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:ZT5VJGVTPBZFIP3D3VNJJLGUKN","short_pith_number":"pith:ZT5VJGVT","canonical_record":{"source":{"id":"1606.07839","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-06-24T21:48:55Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"337b65e7d6725a9ff0523914657684fd0ac33f895b8044bb0184abc4e88c8b4a","abstract_canon_sha256":"b5394fcb7fafa108f805127a9d37a17343f48a2f4c1143982d8d5b6dc93e5e11"},"schema_version":"1.0"},"canonical_sha256":"ccfb549ab37872543f63dd5a94acd45357abf01b2dadd29856862bbd4efd650b","source":{"kind":"arxiv","id":"1606.07839","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1606.07839","created_at":"2026-05-18T01:03:07Z"},{"alias_kind":"arxiv_version","alias_value":"1606.07839v3","created_at":"2026-05-18T01:03:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.07839","created_at":"2026-05-18T01:03:07Z"},{"alias_kind":"pith_short_12","alias_value":"ZT5VJGVTPBZF","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_16","alias_value":"ZT5VJGVTPBZFIP3D","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_8","alias_value":"ZT5VJGVT","created_at":"2026-05-18T12:30:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:ZT5VJGVTPBZFIP3D3VNJJLGUKN","target":"record","payload":{"canonical_record":{"source":{"id":"1606.07839","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-06-24T21:48:55Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"337b65e7d6725a9ff0523914657684fd0ac33f895b8044bb0184abc4e88c8b4a","abstract_canon_sha256":"b5394fcb7fafa108f805127a9d37a17343f48a2f4c1143982d8d5b6dc93e5e11"},"schema_version":"1.0"},"canonical_sha256":"ccfb549ab37872543f63dd5a94acd45357abf01b2dadd29856862bbd4efd650b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:03:07.299066Z","signature_b64":"cGdN4TMRkW+fQOFO80aTfg77DjFJQpovUL05YLb0U1GAijqWU84uTgWXznNI3qgUZP0hMK91EjPvVv71fR5YCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ccfb549ab37872543f63dd5a94acd45357abf01b2dadd29856862bbd4efd650b","last_reissued_at":"2026-05-18T01:03:07.298590Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:03:07.298590Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1606.07839","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":"2026-05-18T01:03:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kUXjWGk5mHzuCMU8tCuDHUh7FUtW8ZDfA49Fs1Ojl6nPYK03vwqjypepYgPWj+MZXO/IfwVUR99o7v6mqK+mAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T01:53:42.337394Z"},"content_sha256":"77eafa1e503fb071a1ac49c77f0a2ffb96abf22ce8997c177ba497d39f83ba47","schema_version":"1.0","event_id":"sha256:77eafa1e503fb071a1ac49c77f0a2ffb96abf22ce8997c177ba497d39f83ba47"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:ZT5VJGVTPBZFIP3D3VNJJLGUKN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.CV","authors_text":"David Crandall, Dhruv Batra, Michael Cogswell, Senthil Purushwalkam, Stefan Lee, Viresh Ranjan","submitted_at":"2016-06-24T21:48:55Z","abstract_excerpt":"Many practical perception systems exist within larger processes that include interactions with users or additional components capable of evaluating the quality of predicted solutions. In these contexts, it is beneficial to provide these oracle mechanisms with multiple highly likely hypotheses rather than a single prediction. In this work, we pose the task of producing multiple outputs as a learning problem over an ensemble of deep networks -- introducing a novel stochastic gradient descent based approach to minimize the loss with respect to an oracle. Our method is simple to implement, agnosti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.07839","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":""},"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":"2026-05-18T01:03:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GN6yegZyQbWwnc9VYAeWldHaOxzQ2oJv7fsmn2iBnEpWrhmsyAMl7hXeISm2DImu8f+o7TGU4Wq9htBlH8Q2AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T01:53:42.338115Z"},"content_sha256":"1fe50180347199c3301e74d1b9de0d89f00bc188bbba8fb7174d17de3c8ecb84","schema_version":"1.0","event_id":"sha256:1fe50180347199c3301e74d1b9de0d89f00bc188bbba8fb7174d17de3c8ecb84"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZT5VJGVTPBZFIP3D3VNJJLGUKN/bundle.json","state_url":"https://pith.science/pith/ZT5VJGVTPBZFIP3D3VNJJLGUKN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZT5VJGVTPBZFIP3D3VNJJLGUKN/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":"2026-05-31T01:53:42Z","links":{"resolver":"https://pith.science/pith/ZT5VJGVTPBZFIP3D3VNJJLGUKN","bundle":"https://pith.science/pith/ZT5VJGVTPBZFIP3D3VNJJLGUKN/bundle.json","state":"https://pith.science/pith/ZT5VJGVTPBZFIP3D3VNJJLGUKN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZT5VJGVTPBZFIP3D3VNJJLGUKN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:ZT5VJGVTPBZFIP3D3VNJJLGUKN","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":"b5394fcb7fafa108f805127a9d37a17343f48a2f4c1143982d8d5b6dc93e5e11","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-06-24T21:48:55Z","title_canon_sha256":"337b65e7d6725a9ff0523914657684fd0ac33f895b8044bb0184abc4e88c8b4a"},"schema_version":"1.0","source":{"id":"1606.07839","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1606.07839","created_at":"2026-05-18T01:03:07Z"},{"alias_kind":"arxiv_version","alias_value":"1606.07839v3","created_at":"2026-05-18T01:03:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.07839","created_at":"2026-05-18T01:03:07Z"},{"alias_kind":"pith_short_12","alias_value":"ZT5VJGVTPBZF","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_16","alias_value":"ZT5VJGVTPBZFIP3D","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_8","alias_value":"ZT5VJGVT","created_at":"2026-05-18T12:30:55Z"}],"graph_snapshots":[{"event_id":"sha256:1fe50180347199c3301e74d1b9de0d89f00bc188bbba8fb7174d17de3c8ecb84","target":"graph","created_at":"2026-05-18T01:03:07Z","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":"Many practical perception systems exist within larger processes that include interactions with users or additional components capable of evaluating the quality of predicted solutions. In these contexts, it is beneficial to provide these oracle mechanisms with multiple highly likely hypotheses rather than a single prediction. In this work, we pose the task of producing multiple outputs as a learning problem over an ensemble of deep networks -- introducing a novel stochastic gradient descent based approach to minimize the loss with respect to an oracle. Our method is simple to implement, agnosti","authors_text":"David Crandall, Dhruv Batra, Michael Cogswell, Senthil Purushwalkam, Stefan Lee, Viresh Ranjan","cross_cats":["cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-06-24T21:48:55Z","title":"Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.07839","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":"sha256:77eafa1e503fb071a1ac49c77f0a2ffb96abf22ce8997c177ba497d39f83ba47","target":"record","created_at":"2026-05-18T01:03:07Z","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":"b5394fcb7fafa108f805127a9d37a17343f48a2f4c1143982d8d5b6dc93e5e11","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-06-24T21:48:55Z","title_canon_sha256":"337b65e7d6725a9ff0523914657684fd0ac33f895b8044bb0184abc4e88c8b4a"},"schema_version":"1.0","source":{"id":"1606.07839","kind":"arxiv","version":3}},"canonical_sha256":"ccfb549ab37872543f63dd5a94acd45357abf01b2dadd29856862bbd4efd650b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ccfb549ab37872543f63dd5a94acd45357abf01b2dadd29856862bbd4efd650b","first_computed_at":"2026-05-18T01:03:07.298590Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:03:07.298590Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cGdN4TMRkW+fQOFO80aTfg77DjFJQpovUL05YLb0U1GAijqWU84uTgWXznNI3qgUZP0hMK91EjPvVv71fR5YCg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:03:07.299066Z","signed_message":"canonical_sha256_bytes"},"source_id":"1606.07839","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:77eafa1e503fb071a1ac49c77f0a2ffb96abf22ce8997c177ba497d39f83ba47","sha256:1fe50180347199c3301e74d1b9de0d89f00bc188bbba8fb7174d17de3c8ecb84"],"state_sha256":"a4024320beb946bba5f41140671c7d01ee90358d21fa78a728bfea4a48643e4a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YVqvAAddoQg6SSnkVqbkDemK4ZYU6TRM0MZzhoXuynvKwFOUKhXpFwNPIqmjUcc7P0DOpDvEJUx8b/DKOZm+BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T01:53:42.343256Z","bundle_sha256":"8093ba83dc707bca723d9357009aedc65922a22e0a0899c9e5e229dc9b3a73a6"}}