{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:DJZVXWESLUB2YGBIMVZHEIZ5S2","short_pith_number":"pith:DJZVXWES","canonical_record":{"source":{"id":"2010.02037","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-05T14:17:32Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"7e6f5a05ee19be187c000969498c339838a6010b7cc7f0e3b3196262340137e4","abstract_canon_sha256":"ca3d52259dd4e2d6b0bd60b9f2c57ef4bb83388c013a802505dc6b67c8510e55"},"schema_version":"1.0"},"canonical_sha256":"1a735bd8925d03ac1828657272233d968d9962f0dfcfb72b99330fa6e5722eb1","source":{"kind":"arxiv","id":"2010.02037","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.02037","created_at":"2026-07-05T01:40:11Z"},{"alias_kind":"arxiv_version","alias_value":"2010.02037v1","created_at":"2026-07-05T01:40:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.02037","created_at":"2026-07-05T01:40:11Z"},{"alias_kind":"pith_short_12","alias_value":"DJZVXWESLUB2","created_at":"2026-07-05T01:40:11Z"},{"alias_kind":"pith_short_16","alias_value":"DJZVXWESLUB2YGBI","created_at":"2026-07-05T01:40:11Z"},{"alias_kind":"pith_short_8","alias_value":"DJZVXWES","created_at":"2026-07-05T01:40:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:DJZVXWESLUB2YGBIMVZHEIZ5S2","target":"record","payload":{"canonical_record":{"source":{"id":"2010.02037","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-05T14:17:32Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"7e6f5a05ee19be187c000969498c339838a6010b7cc7f0e3b3196262340137e4","abstract_canon_sha256":"ca3d52259dd4e2d6b0bd60b9f2c57ef4bb83388c013a802505dc6b67c8510e55"},"schema_version":"1.0"},"canonical_sha256":"1a735bd8925d03ac1828657272233d968d9962f0dfcfb72b99330fa6e5722eb1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:40:11.773549Z","signature_b64":"6K+BXtBdSA9P6vFq2f8UlJN+oM7nCxmAZ1pSpyxcM2sVXmrGZaucPuwMNy+5jnHZQyCqJC54RCSazKIQiaHCDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1a735bd8925d03ac1828657272233d968d9962f0dfcfb72b99330fa6e5722eb1","last_reissued_at":"2026-07-05T01:40:11.773076Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:40:11.773076Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2010.02037","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":"2026-07-05T01:40:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Jvundc5jPy/Ti7zIHFfaqorX9J9S9RHefagJzYeO71Pw0sp5/IYZrmL5tLzUHUQf9FtkiOjdVsmmTW1dT+sjCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T06:27:06.517825Z"},"content_sha256":"18dd7746ceee150573583d60f2262f50adb9448563abe6e41a8c9d372a8740f2","schema_version":"1.0","event_id":"sha256:18dd7746ceee150573583d60f2262f50adb9448563abe6e41a8c9d372a8740f2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:DJZVXWESLUB2YGBIMVZHEIZ5S2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Conditional Negative Sampling for Contrastive Learning of Visual Representations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Chengxu Zhuang, Daniel Yamins, Mike Wu, Milan Mosse, Noah Goodman","submitted_at":"2020-10-05T14:17:32Z","abstract_excerpt":"Recent methods for learning unsupervised visual representations, dubbed contrastive learning, optimize the noise-contrastive estimation (NCE) bound on mutual information between two views of an image. NCE uses randomly sampled negative examples to normalize the objective. In this paper, we show that choosing difficult negatives, or those more similar to the current instance, can yield stronger representations. To do this, we introduce a family of mutual information estimators that sample negatives conditionally -- in a \"ring\" around each positive. We prove that these estimators lower-bound mut"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.02037","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2010.02037/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T01:40:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QI7so8CX4sW79qYHfrSNa7byPZ2UEP9caObT1gL3m2Ed2GCr6YVsrUKdLIFPdN065OeIenoFCX3+GUoNtrWyDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T06:27:06.518561Z"},"content_sha256":"ff4e05f832cd932f865bcfbf69d980320615f835491104fa97e5b62ba375db1f","schema_version":"1.0","event_id":"sha256:ff4e05f832cd932f865bcfbf69d980320615f835491104fa97e5b62ba375db1f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DJZVXWESLUB2YGBIMVZHEIZ5S2/bundle.json","state_url":"https://pith.science/pith/DJZVXWESLUB2YGBIMVZHEIZ5S2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DJZVXWESLUB2YGBIMVZHEIZ5S2/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":"2026-08-10T06:27:06Z","links":{"resolver":"https://pith.science/pith/DJZVXWESLUB2YGBIMVZHEIZ5S2","bundle":"https://pith.science/pith/DJZVXWESLUB2YGBIMVZHEIZ5S2/bundle.json","state":"https://pith.science/pith/DJZVXWESLUB2YGBIMVZHEIZ5S2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DJZVXWESLUB2YGBIMVZHEIZ5S2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:DJZVXWESLUB2YGBIMVZHEIZ5S2","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":"ca3d52259dd4e2d6b0bd60b9f2c57ef4bb83388c013a802505dc6b67c8510e55","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-05T14:17:32Z","title_canon_sha256":"7e6f5a05ee19be187c000969498c339838a6010b7cc7f0e3b3196262340137e4"},"schema_version":"1.0","source":{"id":"2010.02037","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.02037","created_at":"2026-07-05T01:40:11Z"},{"alias_kind":"arxiv_version","alias_value":"2010.02037v1","created_at":"2026-07-05T01:40:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.02037","created_at":"2026-07-05T01:40:11Z"},{"alias_kind":"pith_short_12","alias_value":"DJZVXWESLUB2","created_at":"2026-07-05T01:40:11Z"},{"alias_kind":"pith_short_16","alias_value":"DJZVXWESLUB2YGBI","created_at":"2026-07-05T01:40:11Z"},{"alias_kind":"pith_short_8","alias_value":"DJZVXWES","created_at":"2026-07-05T01:40:11Z"}],"graph_snapshots":[{"event_id":"sha256:ff4e05f832cd932f865bcfbf69d980320615f835491104fa97e5b62ba375db1f","target":"graph","created_at":"2026-07-05T01:40:11Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2010.02037/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent methods for learning unsupervised visual representations, dubbed contrastive learning, optimize the noise-contrastive estimation (NCE) bound on mutual information between two views of an image. NCE uses randomly sampled negative examples to normalize the objective. In this paper, we show that choosing difficult negatives, or those more similar to the current instance, can yield stronger representations. To do this, we introduce a family of mutual information estimators that sample negatives conditionally -- in a \"ring\" around each positive. We prove that these estimators lower-bound mut","authors_text":"Chengxu Zhuang, Daniel Yamins, Mike Wu, Milan Mosse, Noah Goodman","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-05T14:17:32Z","title":"Conditional Negative Sampling for Contrastive Learning of Visual Representations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.02037","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":"sha256:18dd7746ceee150573583d60f2262f50adb9448563abe6e41a8c9d372a8740f2","target":"record","created_at":"2026-07-05T01:40:11Z","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":"ca3d52259dd4e2d6b0bd60b9f2c57ef4bb83388c013a802505dc6b67c8510e55","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-05T14:17:32Z","title_canon_sha256":"7e6f5a05ee19be187c000969498c339838a6010b7cc7f0e3b3196262340137e4"},"schema_version":"1.0","source":{"id":"2010.02037","kind":"arxiv","version":1}},"canonical_sha256":"1a735bd8925d03ac1828657272233d968d9962f0dfcfb72b99330fa6e5722eb1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1a735bd8925d03ac1828657272233d968d9962f0dfcfb72b99330fa6e5722eb1","first_computed_at":"2026-07-05T01:40:11.773076Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:40:11.773076Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6K+BXtBdSA9P6vFq2f8UlJN+oM7nCxmAZ1pSpyxcM2sVXmrGZaucPuwMNy+5jnHZQyCqJC54RCSazKIQiaHCDA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:40:11.773549Z","signed_message":"canonical_sha256_bytes"},"source_id":"2010.02037","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:18dd7746ceee150573583d60f2262f50adb9448563abe6e41a8c9d372a8740f2","sha256:ff4e05f832cd932f865bcfbf69d980320615f835491104fa97e5b62ba375db1f"],"state_sha256":"9bff8f03b43011e24d5ddeb6ae9c1fce3c237c424b71c6aaf3233c0410bc190c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OyQXLcgsfe4QjtXv1XQIGh6OypWQc67u98q3QiPKkVPiUA9tlv9fETNBvA90dlA9zSS9wPxoihXHX0LFw5+DCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T06:27:06.524418Z","bundle_sha256":"dfb206bd749ab2bc73dc85ae53a7202c6a5b1fe12d41e2f9fcbe2ed2e6a164bb"}}