{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:ZVC6D7B24IUQSK5IGBFR2A45QN","short_pith_number":"pith:ZVC6D7B2","schema_version":"1.0","canonical_sha256":"cd45e1fc3ae229092ba8304b1d039d8377016d214486c64fa1a098768e392d90","source":{"kind":"arxiv","id":"2608.07043","version":1},"attestation_state":"computed","paper":{"title":"Tensor Network Kernel Machines: A JAX Framework for Machine Learning and Nonlinear System Identification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.SY","eess.SY"],"primary_cat":"cs.MS","authors_text":"Albert Saiapin, Kim Batselier","submitted_at":"2026-08-07T09:52:05Z","abstract_excerpt":"Developing nonlinear models that are both expressive and computationally efficient remains a challenge in machine learning and nonlinear system identification. Tensor network kernel machines (TNKM) address this challenge by combining nonlinear feature representations with compact low-rank tensor-network parameterizations. However, practical and extensible software frameworks for developing TNKM models remain limited. In this work, we introduce \"tnkm\", an open-source Python library for constructing and training TNKM models using JAX. The library provides a unified interface for combining differ"},"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":"2608.07043","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.MS","submitted_at":"2026-08-07T09:52:05Z","cross_cats_sorted":["cs.LG","cs.SY","eess.SY"],"title_canon_sha256":"56b58c25aea20f1312f1fde1312b9b1cb85411d97cfe4ecc97dca02dffbd8ec9","abstract_canon_sha256":"0df59570c184ecfcbf9a89b5840bafef432197e411a7e8ac31a2293c04ce6cae"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-10T01:12:58.102899Z","signature_b64":"uqGyiCh/03yPSQyRtbM4gsfCwyLGV3Ri/4uTxg749ucvj46ESFuYyNCwgd51gZHCME1Ek+Kb3J45xjHh7rphDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cd45e1fc3ae229092ba8304b1d039d8377016d214486c64fa1a098768e392d90","last_reissued_at":"2026-08-10T01:12:58.100529Z","signature_status":"signed_v1","first_computed_at":"2026-08-10T01:12:58.100529Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Tensor Network Kernel Machines: A JAX Framework for Machine Learning and Nonlinear System Identification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.SY","eess.SY"],"primary_cat":"cs.MS","authors_text":"Albert Saiapin, Kim Batselier","submitted_at":"2026-08-07T09:52:05Z","abstract_excerpt":"Developing nonlinear models that are both expressive and computationally efficient remains a challenge in machine learning and nonlinear system identification. Tensor network kernel machines (TNKM) address this challenge by combining nonlinear feature representations with compact low-rank tensor-network parameterizations. However, practical and extensible software frameworks for developing TNKM models remain limited. In this work, we introduce \"tnkm\", an open-source Python library for constructing and training TNKM models using JAX. The library provides a unified interface for combining differ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.07043","kind":"arxiv","version":1},"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/2608.07043/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":"2608.07043","created_at":"2026-08-10T01:12:58.101483+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.07043v1","created_at":"2026-08-10T01:12:58.101483+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.07043","created_at":"2026-08-10T01:12:58.101483+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZVC6D7B24IUQ","created_at":"2026-08-10T01:12:58.101483+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZVC6D7B24IUQSK5I","created_at":"2026-08-10T01:12:58.101483+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZVC6D7B2","created_at":"2026-08-10T01:12:58.101483+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZVC6D7B24IUQSK5IGBFR2A45QN","json":"https://pith.science/pith/ZVC6D7B24IUQSK5IGBFR2A45QN.json","graph_json":"https://pith.science/api/pith-number/ZVC6D7B24IUQSK5IGBFR2A45QN/graph.json","events_json":"https://pith.science/api/pith-number/ZVC6D7B24IUQSK5IGBFR2A45QN/events.json","paper":"https://pith.science/paper/ZVC6D7B2"},"agent_actions":{"view_html":"https://pith.science/pith/ZVC6D7B24IUQSK5IGBFR2A45QN","download_json":"https://pith.science/pith/ZVC6D7B24IUQSK5IGBFR2A45QN.json","view_paper":"https://pith.science/paper/ZVC6D7B2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.07043&json=true","fetch_graph":"https://pith.science/api/pith-number/ZVC6D7B24IUQSK5IGBFR2A45QN/graph.json","fetch_events":"https://pith.science/api/pith-number/ZVC6D7B24IUQSK5IGBFR2A45QN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZVC6D7B24IUQSK5IGBFR2A45QN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZVC6D7B24IUQSK5IGBFR2A45QN/action/storage_attestation","attest_author":"https://pith.science/pith/ZVC6D7B24IUQSK5IGBFR2A45QN/action/author_attestation","sign_citation":"https://pith.science/pith/ZVC6D7B24IUQSK5IGBFR2A45QN/action/citation_signature","submit_replication":"https://pith.science/pith/ZVC6D7B24IUQSK5IGBFR2A45QN/action/replication_record"}},"created_at":"2026-08-10T01:12:58.101483+00:00","updated_at":"2026-08-10T01:12:58.101483+00:00"}