{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:AYZ33BFXM7JSY2OCA3ZCUSW4A5","short_pith_number":"pith:AYZ33BFX","canonical_record":{"source":{"id":"2306.12370","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-21T16:26:14Z","cross_cats_sorted":[],"title_canon_sha256":"02f5fc9dac1010504c35d3d81704a0743b24aa6192d358d0768cb920dbdd2209","abstract_canon_sha256":"89acf0b2629fcb8bcfb688079bc775d8f57a41085355eca80d31dfafcc124cff"},"schema_version":"1.0"},"canonical_sha256":"0633bd84b767d32c69c206f22a4adc075e9fa0c7d2c4723bdba20d80762c1cc9","source":{"kind":"arxiv","id":"2306.12370","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.12370","created_at":"2026-07-05T07:12:50Z"},{"alias_kind":"arxiv_version","alias_value":"2306.12370v2","created_at":"2026-07-05T07:12:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.12370","created_at":"2026-07-05T07:12:50Z"},{"alias_kind":"pith_short_12","alias_value":"AYZ33BFXM7JS","created_at":"2026-07-05T07:12:50Z"},{"alias_kind":"pith_short_16","alias_value":"AYZ33BFXM7JSY2OC","created_at":"2026-07-05T07:12:50Z"},{"alias_kind":"pith_short_8","alias_value":"AYZ33BFX","created_at":"2026-07-05T07:12:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:AYZ33BFXM7JSY2OCA3ZCUSW4A5","target":"record","payload":{"canonical_record":{"source":{"id":"2306.12370","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-21T16:26:14Z","cross_cats_sorted":[],"title_canon_sha256":"02f5fc9dac1010504c35d3d81704a0743b24aa6192d358d0768cb920dbdd2209","abstract_canon_sha256":"89acf0b2629fcb8bcfb688079bc775d8f57a41085355eca80d31dfafcc124cff"},"schema_version":"1.0"},"canonical_sha256":"0633bd84b767d32c69c206f22a4adc075e9fa0c7d2c4723bdba20d80762c1cc9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:12:50.961629Z","signature_b64":"9qJElbVxUQTMkkVzu7Nw4h7MiX2COxt/EX6tMInDb2l0EwzMpHbMDutrQbijFvUIdqqJrDqL8Yqa6iEiS0vHAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0633bd84b767d32c69c206f22a4adc075e9fa0c7d2c4723bdba20d80762c1cc9","last_reissued_at":"2026-07-05T07:12:50.961198Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:12:50.961198Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2306.12370","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":"2026-07-05T07:12:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hPJf8bQ5KKmmBQEofAd9F/WSsQKF7mRnU3mz6yrHM1VmWWlL8scHFMeqVEnODAfma463shEY3NJZlLgzrUo+AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T05:56:53.288673Z"},"content_sha256":"830d48b32e846f51d088a459efcfc18efd351f0894740e7deb6468a9dd5c92c7","schema_version":"1.0","event_id":"sha256:830d48b32e846f51d088a459efcfc18efd351f0894740e7deb6468a9dd5c92c7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:AYZ33BFXM7JSY2OCA3ZCUSW4A5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Carl Hvarfner, Danny Stoll, Edward Bergman, Frank Hutter, Luigi Nardi, Maciej Janowski, Marius Lindauer, Neeratyoy Mallik","submitted_at":"2023-06-21T16:26:14Z","abstract_excerpt":"Hyperparameters of Deep Learning (DL) pipelines are crucial for their downstream performance. While a large number of methods for Hyperparameter Optimization (HPO) have been developed, their incurred costs are often untenable for modern DL. Consequently, manual experimentation is still the most prevalent approach to optimize hyperparameters, relying on the researcher's intuition, domain knowledge, and cheap preliminary explorations. To resolve this misalignment between HPO algorithms and DL researchers, we propose PriorBand, an HPO algorithm tailored to DL, able to utilize both expert beliefs "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.12370","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/2306.12370/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":"2026-07-05T07:12:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EpPK+TR4+TYLdM46CGrHnGDLxNfBktioZ/PioFPnazUvO6QMosIdYn6hRhDIfo9pFbomc+QzK0WDU1mf6xq0Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T05:56:53.289307Z"},"content_sha256":"d8404ac83bfaca5e960b1ae8283094a316cc2632ba828d5b9b11549e84c15951","schema_version":"1.0","event_id":"sha256:d8404ac83bfaca5e960b1ae8283094a316cc2632ba828d5b9b11549e84c15951"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AYZ33BFXM7JSY2OCA3ZCUSW4A5/bundle.json","state_url":"https://pith.science/pith/AYZ33BFXM7JSY2OCA3ZCUSW4A5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AYZ33BFXM7JSY2OCA3ZCUSW4A5/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":"2026-07-09T05:56:53Z","links":{"resolver":"https://pith.science/pith/AYZ33BFXM7JSY2OCA3ZCUSW4A5","bundle":"https://pith.science/pith/AYZ33BFXM7JSY2OCA3ZCUSW4A5/bundle.json","state":"https://pith.science/pith/AYZ33BFXM7JSY2OCA3ZCUSW4A5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AYZ33BFXM7JSY2OCA3ZCUSW4A5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:AYZ33BFXM7JSY2OCA3ZCUSW4A5","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":"89acf0b2629fcb8bcfb688079bc775d8f57a41085355eca80d31dfafcc124cff","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-21T16:26:14Z","title_canon_sha256":"02f5fc9dac1010504c35d3d81704a0743b24aa6192d358d0768cb920dbdd2209"},"schema_version":"1.0","source":{"id":"2306.12370","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.12370","created_at":"2026-07-05T07:12:50Z"},{"alias_kind":"arxiv_version","alias_value":"2306.12370v2","created_at":"2026-07-05T07:12:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.12370","created_at":"2026-07-05T07:12:50Z"},{"alias_kind":"pith_short_12","alias_value":"AYZ33BFXM7JS","created_at":"2026-07-05T07:12:50Z"},{"alias_kind":"pith_short_16","alias_value":"AYZ33BFXM7JSY2OC","created_at":"2026-07-05T07:12:50Z"},{"alias_kind":"pith_short_8","alias_value":"AYZ33BFX","created_at":"2026-07-05T07:12:50Z"}],"graph_snapshots":[{"event_id":"sha256:d8404ac83bfaca5e960b1ae8283094a316cc2632ba828d5b9b11549e84c15951","target":"graph","created_at":"2026-07-05T07:12:50Z","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/2306.12370/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Hyperparameters of Deep Learning (DL) pipelines are crucial for their downstream performance. While a large number of methods for Hyperparameter Optimization (HPO) have been developed, their incurred costs are often untenable for modern DL. Consequently, manual experimentation is still the most prevalent approach to optimize hyperparameters, relying on the researcher's intuition, domain knowledge, and cheap preliminary explorations. To resolve this misalignment between HPO algorithms and DL researchers, we propose PriorBand, an HPO algorithm tailored to DL, able to utilize both expert beliefs ","authors_text":"Carl Hvarfner, Danny Stoll, Edward Bergman, Frank Hutter, Luigi Nardi, Maciej Janowski, Marius Lindauer, Neeratyoy Mallik","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-21T16:26:14Z","title":"PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.12370","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":"sha256:830d48b32e846f51d088a459efcfc18efd351f0894740e7deb6468a9dd5c92c7","target":"record","created_at":"2026-07-05T07:12:50Z","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":"89acf0b2629fcb8bcfb688079bc775d8f57a41085355eca80d31dfafcc124cff","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-21T16:26:14Z","title_canon_sha256":"02f5fc9dac1010504c35d3d81704a0743b24aa6192d358d0768cb920dbdd2209"},"schema_version":"1.0","source":{"id":"2306.12370","kind":"arxiv","version":2}},"canonical_sha256":"0633bd84b767d32c69c206f22a4adc075e9fa0c7d2c4723bdba20d80762c1cc9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0633bd84b767d32c69c206f22a4adc075e9fa0c7d2c4723bdba20d80762c1cc9","first_computed_at":"2026-07-05T07:12:50.961198Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:12:50.961198Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9qJElbVxUQTMkkVzu7Nw4h7MiX2COxt/EX6tMInDb2l0EwzMpHbMDutrQbijFvUIdqqJrDqL8Yqa6iEiS0vHAA==","signature_status":"signed_v1","signed_at":"2026-07-05T07:12:50.961629Z","signed_message":"canonical_sha256_bytes"},"source_id":"2306.12370","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:830d48b32e846f51d088a459efcfc18efd351f0894740e7deb6468a9dd5c92c7","sha256:d8404ac83bfaca5e960b1ae8283094a316cc2632ba828d5b9b11549e84c15951"],"state_sha256":"f0348c7449aa032f53b2af9f95a3af7fd800f15d74977c51a81d5f24ee3de1ac"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gwhGmglMcOPZp/FLLFw5JwMIJmA7A6vKY+Nf0lHMh1NNIFUlxCxqbyLx8CFtfr03a1ay4MoT7MVPQ/qbzjgaCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-09T05:56:53.292877Z","bundle_sha256":"93f5c27f1da42aa7f243a593af900086b47e668856af0261c5e722180c073845"}}