{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:WMDBQ6H2WDPGHZUWPTLS73NJ3W","short_pith_number":"pith:WMDBQ6H2","schema_version":"1.0","canonical_sha256":"b3061878fab0de63e6967cd72feda9dda0685d687939a48a35fbf85dcb70a9e5","source":{"kind":"arxiv","id":"2501.07145","version":2},"attestation_state":"computed","paper":{"title":"A User's Guide to $\\texttt{KSig}$: GPU-Accelerated Computation of the Signature Kernel","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Csaba T\\'oth, Danilo Jr Dela Cruz, Harald Oberhauser","submitted_at":"2025-01-13T09:11:13Z","abstract_excerpt":"The signature kernel is a positive definite kernel for sequential and temporal data that has become increasingly popular in machine learning applications due to powerful theoretical guarantees, strong empirical performance, and recently introduced various scalable variations. In this chapter, we give a short introduction to $\\texttt{KSig}$, a $\\texttt{Scikit-Learn}$ compatible Python package that implements various GPU-accelerated algorithms for computing signature kernels, and performing downstream learning tasks. We also introduce a new algorithm based on tensor sketches which gives strong p"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2501.07145","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-01-13T09:11:13Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"2cc5080331173845a751508f675933c14f9153d978f94ff642b8c21c47919e5a","abstract_canon_sha256":"e19e88a42119420554888b44f9c3c4e654fb2c9eebe29874b9ed9286902a0089"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:00:46.930355Z","signature_b64":"Pt9HEwRnrejepCpnnA3lJ2o7/K62gQrDB1BM/bjIKOZ8CHgT8Khf08xHfj8UHlHGuYBSrCuzSnpkc8LvNAPNCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b3061878fab0de63e6967cd72feda9dda0685d687939a48a35fbf85dcb70a9e5","last_reissued_at":"2026-07-05T10:00:46.929932Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:00:46.929932Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A User's Guide to $\\texttt{KSig}$: GPU-Accelerated Computation of the Signature Kernel","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Csaba T\\'oth, Danilo Jr Dela Cruz, Harald Oberhauser","submitted_at":"2025-01-13T09:11:13Z","abstract_excerpt":"The signature kernel is a positive definite kernel for sequential and temporal data that has become increasingly popular in machine learning applications due to powerful theoretical guarantees, strong empirical performance, and recently introduced various scalable variations. In this chapter, we give a short introduction to $\\texttt{KSig}$, a $\\texttt{Scikit-Learn}$ compatible Python package that implements various GPU-accelerated algorithms for computing signature kernels, and performing downstream learning tasks. We also introduce a new algorithm based on tensor sketches which gives strong p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.07145","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/2501.07145/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2501.07145","created_at":"2026-07-05T10:00:46.929989+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.07145v2","created_at":"2026-07-05T10:00:46.929989+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.07145","created_at":"2026-07-05T10:00:46.929989+00:00"},{"alias_kind":"pith_short_12","alias_value":"WMDBQ6H2WDPG","created_at":"2026-07-05T10:00:46.929989+00:00"},{"alias_kind":"pith_short_16","alias_value":"WMDBQ6H2WDPGHZUW","created_at":"2026-07-05T10:00:46.929989+00:00"},{"alias_kind":"pith_short_8","alias_value":"WMDBQ6H2","created_at":"2026-07-05T10:00:46.929989+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.25826","citing_title":"Branched Signature Kernel Solvers for ODEs with rough Single-Trajectory signals","ref_index":62,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WMDBQ6H2WDPGHZUWPTLS73NJ3W","json":"https://pith.science/pith/WMDBQ6H2WDPGHZUWPTLS73NJ3W.json","graph_json":"https://pith.science/api/pith-number/WMDBQ6H2WDPGHZUWPTLS73NJ3W/graph.json","events_json":"https://pith.science/api/pith-number/WMDBQ6H2WDPGHZUWPTLS73NJ3W/events.json","paper":"https://pith.science/paper/WMDBQ6H2"},"agent_actions":{"view_html":"https://pith.science/pith/WMDBQ6H2WDPGHZUWPTLS73NJ3W","download_json":"https://pith.science/pith/WMDBQ6H2WDPGHZUWPTLS73NJ3W.json","view_paper":"https://pith.science/paper/WMDBQ6H2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.07145&json=true","fetch_graph":"https://pith.science/api/pith-number/WMDBQ6H2WDPGHZUWPTLS73NJ3W/graph.json","fetch_events":"https://pith.science/api/pith-number/WMDBQ6H2WDPGHZUWPTLS73NJ3W/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WMDBQ6H2WDPGHZUWPTLS73NJ3W/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WMDBQ6H2WDPGHZUWPTLS73NJ3W/action/storage_attestation","attest_author":"https://pith.science/pith/WMDBQ6H2WDPGHZUWPTLS73NJ3W/action/author_attestation","sign_citation":"https://pith.science/pith/WMDBQ6H2WDPGHZUWPTLS73NJ3W/action/citation_signature","submit_replication":"https://pith.science/pith/WMDBQ6H2WDPGHZUWPTLS73NJ3W/action/replication_record"}},"created_at":"2026-07-05T10:00:46.929989+00:00","updated_at":"2026-07-05T10:00:46.929989+00:00"}