{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:QMYVMWT3XGLBVIF4ABBS5IBQ7F","short_pith_number":"pith:QMYVMWT3","canonical_record":{"source":{"id":"1904.12286","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-28T09:35:50Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"c3b9ec07ef139652e942f0f98fa5ebc348c0e877f2e101e7277b34cecfad2481","abstract_canon_sha256":"14c027158da81146087980d57a6038e20e680d1d9a1d3d66ce4b11e1b09b91d1"},"schema_version":"1.0"},"canonical_sha256":"8331565a7bb9961aa0bc00432ea030f9715d9bd37e2c63f1a45b0c6e1fee0f55","source":{"kind":"arxiv","id":"1904.12286","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.12286","created_at":"2026-07-05T00:32:12Z"},{"alias_kind":"arxiv_version","alias_value":"1904.12286v2","created_at":"2026-07-05T00:32:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.12286","created_at":"2026-07-05T00:32:12Z"},{"alias_kind":"pith_short_12","alias_value":"QMYVMWT3XGLB","created_at":"2026-07-05T00:32:12Z"},{"alias_kind":"pith_short_16","alias_value":"QMYVMWT3XGLBVIF4","created_at":"2026-07-05T00:32:12Z"},{"alias_kind":"pith_short_8","alias_value":"QMYVMWT3","created_at":"2026-07-05T00:32:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:QMYVMWT3XGLBVIF4ABBS5IBQ7F","target":"record","payload":{"canonical_record":{"source":{"id":"1904.12286","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-28T09:35:50Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"c3b9ec07ef139652e942f0f98fa5ebc348c0e877f2e101e7277b34cecfad2481","abstract_canon_sha256":"14c027158da81146087980d57a6038e20e680d1d9a1d3d66ce4b11e1b09b91d1"},"schema_version":"1.0"},"canonical_sha256":"8331565a7bb9961aa0bc00432ea030f9715d9bd37e2c63f1a45b0c6e1fee0f55","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:32:12.162909Z","signature_b64":"7d2wMEtbK3LmJHua0nhvExohBtP7P9+WfrV5TdYmr2yYp96x+XLjOcLlyImShdYsUo7+ix2Nq6uSyiJDun1GBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8331565a7bb9961aa0bc00432ea030f9715d9bd37e2c63f1a45b0c6e1fee0f55","last_reissued_at":"2026-07-05T00:32:12.162512Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:32:12.162512Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1904.12286","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:32:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cBKv9Pa8hN4ZitsJJCJdd7fSwjmtLfOV42I1ufySr1YDase88AJCtFwFzDAXd6BH81Mv/o/ZgmDtD02dbbSmCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T16:07:33.182860Z"},"content_sha256":"685d629a0a76f1c50a3129c6ee03be322ea07a7bb3b3eb6cbc49417f848659d7","schema_version":"1.0","event_id":"sha256:685d629a0a76f1c50a3129c6ee03be322ea07a7bb3b3eb6cbc49417f848659d7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:QMYVMWT3XGLBVIF4ABBS5IBQ7F","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep pNML: Predictive Normalized Maximum Likelihood for Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Koby Bibas, Meir Feder, Yaniv Fogel","submitted_at":"2019-04-28T09:35:50Z","abstract_excerpt":"The Predictive Normalized Maximum Likelihood (pNML) scheme has been recently suggested for universal learning in the individual setting, where both the training and test samples are individual data. The goal of universal learning is to compete with a ``genie'' or reference learner that knows the data values, but is restricted to use a learner from a given model class. The pNML minimizes the associated regret for any possible value of the unknown label. Furthermore, its min-max regret can serve as a pointwise measure of learnability for the specific training and data sample. In this work we exa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.12286","kind":"arxiv","version":2},"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/1904.12286/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":"2026-07-05T00:32:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"T17EEX1RdShf25IDY9Obl4K1QaOrNIDUPmi4327bRPxpyfH3tDK42kvfHg8yi19i9SJ/Cv5Zw6nA8i7D+5itDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T16:07:33.183371Z"},"content_sha256":"e223f87c0a942076e73f04970d8234a7630dd23045b5c4b23436e7b81acccb3e","schema_version":"1.0","event_id":"sha256:e223f87c0a942076e73f04970d8234a7630dd23045b5c4b23436e7b81acccb3e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QMYVMWT3XGLBVIF4ABBS5IBQ7F/bundle.json","state_url":"https://pith.science/pith/QMYVMWT3XGLBVIF4ABBS5IBQ7F/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QMYVMWT3XGLBVIF4ABBS5IBQ7F/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":"2026-08-12T16:07:33Z","links":{"resolver":"https://pith.science/pith/QMYVMWT3XGLBVIF4ABBS5IBQ7F","bundle":"https://pith.science/pith/QMYVMWT3XGLBVIF4ABBS5IBQ7F/bundle.json","state":"https://pith.science/pith/QMYVMWT3XGLBVIF4ABBS5IBQ7F/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QMYVMWT3XGLBVIF4ABBS5IBQ7F/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:QMYVMWT3XGLBVIF4ABBS5IBQ7F","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":"14c027158da81146087980d57a6038e20e680d1d9a1d3d66ce4b11e1b09b91d1","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-28T09:35:50Z","title_canon_sha256":"c3b9ec07ef139652e942f0f98fa5ebc348c0e877f2e101e7277b34cecfad2481"},"schema_version":"1.0","source":{"id":"1904.12286","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.12286","created_at":"2026-07-05T00:32:12Z"},{"alias_kind":"arxiv_version","alias_value":"1904.12286v2","created_at":"2026-07-05T00:32:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.12286","created_at":"2026-07-05T00:32:12Z"},{"alias_kind":"pith_short_12","alias_value":"QMYVMWT3XGLB","created_at":"2026-07-05T00:32:12Z"},{"alias_kind":"pith_short_16","alias_value":"QMYVMWT3XGLBVIF4","created_at":"2026-07-05T00:32:12Z"},{"alias_kind":"pith_short_8","alias_value":"QMYVMWT3","created_at":"2026-07-05T00:32:12Z"}],"graph_snapshots":[{"event_id":"sha256:e223f87c0a942076e73f04970d8234a7630dd23045b5c4b23436e7b81acccb3e","target":"graph","created_at":"2026-07-05T00:32:12Z","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/1904.12286/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The Predictive Normalized Maximum Likelihood (pNML) scheme has been recently suggested for universal learning in the individual setting, where both the training and test samples are individual data. The goal of universal learning is to compete with a ``genie'' or reference learner that knows the data values, but is restricted to use a learner from a given model class. The pNML minimizes the associated regret for any possible value of the unknown label. Furthermore, its min-max regret can serve as a pointwise measure of learnability for the specific training and data sample. In this work we exa","authors_text":"Koby Bibas, Meir Feder, Yaniv Fogel","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-28T09:35:50Z","title":"Deep pNML: Predictive Normalized Maximum Likelihood for Deep Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.12286","kind":"arxiv","version":2},"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":"sha256:685d629a0a76f1c50a3129c6ee03be322ea07a7bb3b3eb6cbc49417f848659d7","target":"record","created_at":"2026-07-05T00:32:12Z","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":"14c027158da81146087980d57a6038e20e680d1d9a1d3d66ce4b11e1b09b91d1","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-28T09:35:50Z","title_canon_sha256":"c3b9ec07ef139652e942f0f98fa5ebc348c0e877f2e101e7277b34cecfad2481"},"schema_version":"1.0","source":{"id":"1904.12286","kind":"arxiv","version":2}},"canonical_sha256":"8331565a7bb9961aa0bc00432ea030f9715d9bd37e2c63f1a45b0c6e1fee0f55","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8331565a7bb9961aa0bc00432ea030f9715d9bd37e2c63f1a45b0c6e1fee0f55","first_computed_at":"2026-07-05T00:32:12.162512Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:32:12.162512Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7d2wMEtbK3LmJHua0nhvExohBtP7P9+WfrV5TdYmr2yYp96x+XLjOcLlyImShdYsUo7+ix2Nq6uSyiJDun1GBg==","signature_status":"signed_v1","signed_at":"2026-07-05T00:32:12.162909Z","signed_message":"canonical_sha256_bytes"},"source_id":"1904.12286","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:685d629a0a76f1c50a3129c6ee03be322ea07a7bb3b3eb6cbc49417f848659d7","sha256:e223f87c0a942076e73f04970d8234a7630dd23045b5c4b23436e7b81acccb3e"],"state_sha256":"a634b4312ff4bea03e4a640f5401adbbc24fa639c56bc52b824ef98b497cb19b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LoRFcQZeTLaG8pUboucHh7dgfiVH6VS2yXcK454x1cPf1cwEd9KPqE9Wuy6qY0hT4hNGPzzo7wJnf3byNuu5Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T16:07:33.188505Z","bundle_sha256":"6221d87cd9997baa51397d306d332b78e2ead87f14af8d0e7ee68f14de8d3321"}}