{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:VMJXILSISBZJTF4B4C76SBQO2H","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":"399433e30ac1a1e176b609fcaf8b2426da7c7bb7141d7708b68229f28070ed1e","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2022-02-05T21:01:49Z","title_canon_sha256":"b5ab39a3ab0ae4c535613fa799fdb77fd4ad3f9b269bb4158cabbae26a8e7144"},"schema_version":"1.0","source":{"id":"2202.02641","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.02641","created_at":"2026-07-05T03:57:43Z"},{"alias_kind":"arxiv_version","alias_value":"2202.02641v2","created_at":"2026-07-05T03:57:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.02641","created_at":"2026-07-05T03:57:43Z"},{"alias_kind":"pith_short_12","alias_value":"VMJXILSISBZJ","created_at":"2026-07-05T03:57:43Z"},{"alias_kind":"pith_short_16","alias_value":"VMJXILSISBZJTF4B","created_at":"2026-07-05T03:57:43Z"},{"alias_kind":"pith_short_8","alias_value":"VMJXILSI","created_at":"2026-07-05T03:57:43Z"}],"graph_snapshots":[{"event_id":"sha256:e04c103255278574979ff3e1e62ad4d738205547e4c96f07e2ed40d8a0f0f157","target":"graph","created_at":"2026-07-05T03:57:43Z","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/2202.02641/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Modern machine learning techniques commonly rely on complex, high-dimensional embedding representations to capture underlying structure in the data and improve performance. In order to characterize model flaws and choose a desirable representation, model builders often need to compare across multiple embedding spaces, a challenging analytical task supported by few existing tools. We first interviewed nine embedding experts in a variety of fields to characterize the diverse challenges they face and techniques they use when analyzing embedding spaces. Informed by these perspectives, we developed","authors_text":"Adam Perer, Venkatesh Sivaraman, Yiwei Wu","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2022-02-05T21:01:49Z","title":"Emblaze: Illuminating Machine Learning Representations through Interactive Comparison of Embedding Spaces"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.02641","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:b1c2aa6891562c7f8ab5f8c53248205956c7722a2703e3ecd047c6e83294341a","target":"record","created_at":"2026-07-05T03:57:43Z","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":"399433e30ac1a1e176b609fcaf8b2426da7c7bb7141d7708b68229f28070ed1e","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2022-02-05T21:01:49Z","title_canon_sha256":"b5ab39a3ab0ae4c535613fa799fdb77fd4ad3f9b269bb4158cabbae26a8e7144"},"schema_version":"1.0","source":{"id":"2202.02641","kind":"arxiv","version":2}},"canonical_sha256":"ab13742e489072999781e0bfe9060ed1f858775e418e4f6ae2f2ae199cbea99c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ab13742e489072999781e0bfe9060ed1f858775e418e4f6ae2f2ae199cbea99c","first_computed_at":"2026-07-05T03:57:43.677022Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:57:43.677022Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Oo9TnP97NRWvPciH4ncqKsSlZw1WDA2+YHqsDQz8PAZ1ftnAmAmsCTsed6eyeJgEZV8TeoyolqFd5vsBQh40CQ==","signature_status":"signed_v1","signed_at":"2026-07-05T03:57:43.677576Z","signed_message":"canonical_sha256_bytes"},"source_id":"2202.02641","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b1c2aa6891562c7f8ab5f8c53248205956c7722a2703e3ecd047c6e83294341a","sha256:e04c103255278574979ff3e1e62ad4d738205547e4c96f07e2ed40d8a0f0f157"],"state_sha256":"abaf011957f054cb4852f9975191cf70c9a182b8bb715236ea60c015003c23ee"}