{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:K53GI27IDFN4JDN7JNDC3PAHCH","short_pith_number":"pith:K53GI27I","canonical_record":{"source":{"id":"1511.06314","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-19T19:19:58Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"feea8ae58c9e10817e38c4836c0be948a95a2e96feb17b33fa4847b874c8bcaf","abstract_canon_sha256":"5dc5600768215955abac796f0341408f6cc23bfe57bf339e21f8721f66747faf"},"schema_version":"1.0"},"canonical_sha256":"5776646be8195bc48dbf4b462dbc0711fdcc2373ac4e0505ef3580fb961e1b58","source":{"kind":"arxiv","id":"1511.06314","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1511.06314","created_at":"2026-05-18T01:26:25Z"},{"alias_kind":"arxiv_version","alias_value":"1511.06314v1","created_at":"2026-05-18T01:26:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.06314","created_at":"2026-05-18T01:26:25Z"},{"alias_kind":"pith_short_12","alias_value":"K53GI27IDFN4","created_at":"2026-05-18T12:29:27Z"},{"alias_kind":"pith_short_16","alias_value":"K53GI27IDFN4JDN7","created_at":"2026-05-18T12:29:27Z"},{"alias_kind":"pith_short_8","alias_value":"K53GI27I","created_at":"2026-05-18T12:29:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:K53GI27IDFN4JDN7JNDC3PAHCH","target":"record","payload":{"canonical_record":{"source":{"id":"1511.06314","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-19T19:19:58Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"feea8ae58c9e10817e38c4836c0be948a95a2e96feb17b33fa4847b874c8bcaf","abstract_canon_sha256":"5dc5600768215955abac796f0341408f6cc23bfe57bf339e21f8721f66747faf"},"schema_version":"1.0"},"canonical_sha256":"5776646be8195bc48dbf4b462dbc0711fdcc2373ac4e0505ef3580fb961e1b58","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:26:25.924755Z","signature_b64":"LWdVyj3cwLGRjuFT3pyBZRgI1DS/Yc+N/0dKkWAf+yjLku7k3bE4puyeS4yjGfOzh2sOovyEdNmcQUS1DHRWAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5776646be8195bc48dbf4b462dbc0711fdcc2373ac4e0505ef3580fb961e1b58","last_reissued_at":"2026-05-18T01:26:25.924218Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:26:25.924218Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1511.06314","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":"2026-05-18T01:26:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2sarDx2gh/VT1PYlrhLkfm8peQEYHwZsDO8s0pbtGWs90+QGvJ19xL+MTiOcBlGQIp3Ly+UXS1NpLh3Zg44KCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-20T15:35:18.994199Z"},"content_sha256":"75b6fbeb2dc9a991aa55d3f1ebbb9fee14410346be9c2d7d298fc4c113cfe53a","schema_version":"1.0","event_id":"sha256:75b6fbeb2dc9a991aa55d3f1ebbb9fee14410346be9c2d7d298fc4c113cfe53a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:K53GI27IDFN4JDN7JNDC3PAHCH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"cs.CV","authors_text":"David Crandall, Dhruv Batra, Michael Cogswell, Senthil Purushwalkam, Stefan Lee","submitted_at":"2015-11-19T19:19:58Z","abstract_excerpt":"Convolutional Neural Networks have achieved state-of-the-art performance on a wide range of tasks. Most benchmarks are led by ensembles of these powerful learners, but ensembling is typically treated as a post-hoc procedure implemented by averaging independently trained models with model variation induced by bagging or random initialization. In this paper, we rigorously treat ensembling as a first-class problem to explicitly address the question: what are the best strategies to create an ensemble? We first compare a large number of ensembling strategies, and then propose and evaluate novel str"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.06314","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":"2026-05-18T01:26:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8dTjkai286XE00+my9gfIM8u3N9RlyVEBRfGn+5TWvp4i6INRJIObZhNaRYgQdjues96amImXbAfuqlsT+zbBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-20T15:35:18.994566Z"},"content_sha256":"d561b8def27b49cd3799fd2a7726c9f9e32a7f6692d7245f5281296e5c8db5ac","schema_version":"1.0","event_id":"sha256:d561b8def27b49cd3799fd2a7726c9f9e32a7f6692d7245f5281296e5c8db5ac"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/K53GI27IDFN4JDN7JNDC3PAHCH/bundle.json","state_url":"https://pith.science/pith/K53GI27IDFN4JDN7JNDC3PAHCH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/K53GI27IDFN4JDN7JNDC3PAHCH/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":"2026-06-20T15:35:18Z","links":{"resolver":"https://pith.science/pith/K53GI27IDFN4JDN7JNDC3PAHCH","bundle":"https://pith.science/pith/K53GI27IDFN4JDN7JNDC3PAHCH/bundle.json","state":"https://pith.science/pith/K53GI27IDFN4JDN7JNDC3PAHCH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/K53GI27IDFN4JDN7JNDC3PAHCH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:K53GI27IDFN4JDN7JNDC3PAHCH","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":"5dc5600768215955abac796f0341408f6cc23bfe57bf339e21f8721f66747faf","cross_cats_sorted":["cs.LG","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-19T19:19:58Z","title_canon_sha256":"feea8ae58c9e10817e38c4836c0be948a95a2e96feb17b33fa4847b874c8bcaf"},"schema_version":"1.0","source":{"id":"1511.06314","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1511.06314","created_at":"2026-05-18T01:26:25Z"},{"alias_kind":"arxiv_version","alias_value":"1511.06314v1","created_at":"2026-05-18T01:26:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.06314","created_at":"2026-05-18T01:26:25Z"},{"alias_kind":"pith_short_12","alias_value":"K53GI27IDFN4","created_at":"2026-05-18T12:29:27Z"},{"alias_kind":"pith_short_16","alias_value":"K53GI27IDFN4JDN7","created_at":"2026-05-18T12:29:27Z"},{"alias_kind":"pith_short_8","alias_value":"K53GI27I","created_at":"2026-05-18T12:29:27Z"}],"graph_snapshots":[{"event_id":"sha256:d561b8def27b49cd3799fd2a7726c9f9e32a7f6692d7245f5281296e5c8db5ac","target":"graph","created_at":"2026-05-18T01:26:25Z","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":"Convolutional Neural Networks have achieved state-of-the-art performance on a wide range of tasks. Most benchmarks are led by ensembles of these powerful learners, but ensembling is typically treated as a post-hoc procedure implemented by averaging independently trained models with model variation induced by bagging or random initialization. In this paper, we rigorously treat ensembling as a first-class problem to explicitly address the question: what are the best strategies to create an ensemble? We first compare a large number of ensembling strategies, and then propose and evaluate novel str","authors_text":"David Crandall, Dhruv Batra, Michael Cogswell, Senthil Purushwalkam, Stefan Lee","cross_cats":["cs.LG","cs.NE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-19T19:19:58Z","title":"Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.06314","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":"sha256:75b6fbeb2dc9a991aa55d3f1ebbb9fee14410346be9c2d7d298fc4c113cfe53a","target":"record","created_at":"2026-05-18T01:26:25Z","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":"5dc5600768215955abac796f0341408f6cc23bfe57bf339e21f8721f66747faf","cross_cats_sorted":["cs.LG","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-19T19:19:58Z","title_canon_sha256":"feea8ae58c9e10817e38c4836c0be948a95a2e96feb17b33fa4847b874c8bcaf"},"schema_version":"1.0","source":{"id":"1511.06314","kind":"arxiv","version":1}},"canonical_sha256":"5776646be8195bc48dbf4b462dbc0711fdcc2373ac4e0505ef3580fb961e1b58","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5776646be8195bc48dbf4b462dbc0711fdcc2373ac4e0505ef3580fb961e1b58","first_computed_at":"2026-05-18T01:26:25.924218Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:26:25.924218Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LWdVyj3cwLGRjuFT3pyBZRgI1DS/Yc+N/0dKkWAf+yjLku7k3bE4puyeS4yjGfOzh2sOovyEdNmcQUS1DHRWAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T01:26:25.924755Z","signed_message":"canonical_sha256_bytes"},"source_id":"1511.06314","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:75b6fbeb2dc9a991aa55d3f1ebbb9fee14410346be9c2d7d298fc4c113cfe53a","sha256:d561b8def27b49cd3799fd2a7726c9f9e32a7f6692d7245f5281296e5c8db5ac"],"state_sha256":"ac489be8969862b2548bd95cf9ad9c7da241ba44f0654a5f59015bf00b547b2c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"a3Dy71vD0KTBE1xwRP3t7MONoXGf9VtRcHHx5XMfA7eFF6sbXbt8xp9fnyT7mPNNxx6y6uhjwLFKXA9GoQ7XCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-20T15:35:18.996612Z","bundle_sha256":"23d0a2d30c59c35c6690abdbc71d5ac46d1defb0f1baaae33aa873cb1f19f50c"}}