{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:7TZJLJH6NBJHCPNPRLI7O3L4XO","short_pith_number":"pith:7TZJLJH6","canonical_record":{"source":{"id":"1711.09325","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-26T02:50:39Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"9d4bd948d605a6178ffa316badc42a8bcaf79196b135253217f2aa881ded2a9f","abstract_canon_sha256":"b1ec0e660d595f948c88c9d41ea8cce3444220602f6ab924c2ac997e2eefd48b"},"schema_version":"1.0"},"canonical_sha256":"fcf295a4fe6852713daf8ad1f76d7cbba14cfd2c62d4f8532bebbee055755098","source":{"kind":"arxiv","id":"1711.09325","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.09325","created_at":"2026-05-18T00:22:39Z"},{"alias_kind":"arxiv_version","alias_value":"1711.09325v3","created_at":"2026-05-18T00:22:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.09325","created_at":"2026-05-18T00:22:39Z"},{"alias_kind":"pith_short_12","alias_value":"7TZJLJH6NBJH","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_16","alias_value":"7TZJLJH6NBJHCPNP","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_8","alias_value":"7TZJLJH6","created_at":"2026-05-18T12:31:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:7TZJLJH6NBJHCPNPRLI7O3L4XO","target":"record","payload":{"canonical_record":{"source":{"id":"1711.09325","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-26T02:50:39Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"9d4bd948d605a6178ffa316badc42a8bcaf79196b135253217f2aa881ded2a9f","abstract_canon_sha256":"b1ec0e660d595f948c88c9d41ea8cce3444220602f6ab924c2ac997e2eefd48b"},"schema_version":"1.0"},"canonical_sha256":"fcf295a4fe6852713daf8ad1f76d7cbba14cfd2c62d4f8532bebbee055755098","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:22:39.170527Z","signature_b64":"JO8j2fH5Rob1a2QyW8bWMShTSb06T5mAWF+exv0VVnrD8WnvrBDkOrWKYrX7HPYoErmkiZDzt5+/dFBEkE0FBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fcf295a4fe6852713daf8ad1f76d7cbba14cfd2c62d4f8532bebbee055755098","last_reissued_at":"2026-05-18T00:22:39.170198Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:22:39.170198Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1711.09325","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":"2026-05-18T00:22:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kNYXalLDll5UGHkONOKKT23csCAtbLF7BmWGcoF2BWmUP9vVtwT3Zs2uBzBdXfSW5u5prLFXmyFhf4xHqf+KDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T16:00:33.145779Z"},"content_sha256":"652aa13efb71d851d652e052a3c4fec42936a20636aafd508dc91e43d6290f38","schema_version":"1.0","event_id":"sha256:652aa13efb71d851d652e052a3c4fec42936a20636aafd508dc91e43d6290f38"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:7TZJLJH6NBJHCPNPRLI7O3L4XO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Honglak Lee, Jinwoo Shin, Kibok Lee, Kimin Lee","submitted_at":"2017-11-26T02:50:39Z","abstract_excerpt":"The problem of detecting whether a test sample is from in-distribution (i.e., training distribution by a classifier) or out-of-distribution sufficiently different from it arises in many real-world machine learning applications. However, the state-of-art deep neural networks are known to be highly overconfident in their predictions, i.e., do not distinguish in- and out-of-distributions. Recently, to handle this issue, several threshold-based detectors have been proposed given pre-trained neural classifiers. However, the performance of prior works highly depends on how to train the classifiers s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.09325","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":"2026-05-18T00:22:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sLS7xoxBB0d3YRkDpIbIxlMQ+D7qbIXg926d/RTGt0Kc7NnwpVXDK9uDw7EoJNSzmjHD8kLQtucn+lMrsTYjDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T16:00:33.146482Z"},"content_sha256":"17048cb2f65f27db94dc7a42fda0d449b286b615c6437781c02509254a43fb26","schema_version":"1.0","event_id":"sha256:17048cb2f65f27db94dc7a42fda0d449b286b615c6437781c02509254a43fb26"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7TZJLJH6NBJHCPNPRLI7O3L4XO/bundle.json","state_url":"https://pith.science/pith/7TZJLJH6NBJHCPNPRLI7O3L4XO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7TZJLJH6NBJHCPNPRLI7O3L4XO/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":"2026-06-08T16:00:33Z","links":{"resolver":"https://pith.science/pith/7TZJLJH6NBJHCPNPRLI7O3L4XO","bundle":"https://pith.science/pith/7TZJLJH6NBJHCPNPRLI7O3L4XO/bundle.json","state":"https://pith.science/pith/7TZJLJH6NBJHCPNPRLI7O3L4XO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7TZJLJH6NBJHCPNPRLI7O3L4XO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:7TZJLJH6NBJHCPNPRLI7O3L4XO","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":"b1ec0e660d595f948c88c9d41ea8cce3444220602f6ab924c2ac997e2eefd48b","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-26T02:50:39Z","title_canon_sha256":"9d4bd948d605a6178ffa316badc42a8bcaf79196b135253217f2aa881ded2a9f"},"schema_version":"1.0","source":{"id":"1711.09325","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.09325","created_at":"2026-05-18T00:22:39Z"},{"alias_kind":"arxiv_version","alias_value":"1711.09325v3","created_at":"2026-05-18T00:22:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.09325","created_at":"2026-05-18T00:22:39Z"},{"alias_kind":"pith_short_12","alias_value":"7TZJLJH6NBJH","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_16","alias_value":"7TZJLJH6NBJHCPNP","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_8","alias_value":"7TZJLJH6","created_at":"2026-05-18T12:31:05Z"}],"graph_snapshots":[{"event_id":"sha256:17048cb2f65f27db94dc7a42fda0d449b286b615c6437781c02509254a43fb26","target":"graph","created_at":"2026-05-18T00:22:39Z","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":"The problem of detecting whether a test sample is from in-distribution (i.e., training distribution by a classifier) or out-of-distribution sufficiently different from it arises in many real-world machine learning applications. However, the state-of-art deep neural networks are known to be highly overconfident in their predictions, i.e., do not distinguish in- and out-of-distributions. Recently, to handle this issue, several threshold-based detectors have been proposed given pre-trained neural classifiers. However, the performance of prior works highly depends on how to train the classifiers s","authors_text":"Honglak Lee, Jinwoo Shin, Kibok Lee, Kimin Lee","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-26T02:50:39Z","title":"Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.09325","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":"sha256:652aa13efb71d851d652e052a3c4fec42936a20636aafd508dc91e43d6290f38","target":"record","created_at":"2026-05-18T00:22:39Z","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":"b1ec0e660d595f948c88c9d41ea8cce3444220602f6ab924c2ac997e2eefd48b","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-26T02:50:39Z","title_canon_sha256":"9d4bd948d605a6178ffa316badc42a8bcaf79196b135253217f2aa881ded2a9f"},"schema_version":"1.0","source":{"id":"1711.09325","kind":"arxiv","version":3}},"canonical_sha256":"fcf295a4fe6852713daf8ad1f76d7cbba14cfd2c62d4f8532bebbee055755098","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fcf295a4fe6852713daf8ad1f76d7cbba14cfd2c62d4f8532bebbee055755098","first_computed_at":"2026-05-18T00:22:39.170198Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:22:39.170198Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JO8j2fH5Rob1a2QyW8bWMShTSb06T5mAWF+exv0VVnrD8WnvrBDkOrWKYrX7HPYoErmkiZDzt5+/dFBEkE0FBg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:22:39.170527Z","signed_message":"canonical_sha256_bytes"},"source_id":"1711.09325","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:652aa13efb71d851d652e052a3c4fec42936a20636aafd508dc91e43d6290f38","sha256:17048cb2f65f27db94dc7a42fda0d449b286b615c6437781c02509254a43fb26"],"state_sha256":"96d9fd9c61b7bce0beb06270a23d369a6903e008461add3d6957cb9b87c582f7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eUectAzzy/qrc/garmWEknzLBH6gpcdzZ24hwmpwqW+hLM5DCa2ItjVrzlcS2GLQ/MX/l5kKG6V+pFaE9zXHCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T16:00:33.150219Z","bundle_sha256":"aa292805c37b0735c4ecb5dbe5e4646dcbd3068233706a25b343c6329cf00e32"}}