{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:X4BAVUAP25CBZKC5VUKWC5KF3V","short_pith_number":"pith:X4BAVUAP","schema_version":"1.0","canonical_sha256":"bf020ad00fd7441ca85dad15617545dd7cf20a6c3338674cf3a44d500f8a5d36","source":{"kind":"arxiv","id":"2303.16214","version":1},"attestation_state":"computed","paper":{"title":"Tetra-AML: Automatic Machine Learning via Tensor Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["quant-ph"],"primary_cat":"cs.LG","authors_text":"A. Melnikov, A. Naumov, Ar. Melnikov, F. Oxanichenko, K. Izmailov, M. Perelshtein, M. Pflitsch, V. Abronin","submitted_at":"2023-03-28T12:56:54Z","abstract_excerpt":"Neural networks have revolutionized many aspects of society but in the era of huge models with billions of parameters, optimizing and deploying them for commercial applications can require significant computational and financial resources. To address these challenges, we introduce the Tetra-AML toolbox, which automates neural architecture search and hyperparameter optimization via a custom-developed black-box Tensor train Optimization algorithm, TetraOpt. The toolbox also provides model compression through quantization and pruning, augmented by compression using tensor networks. Here, we analy"},"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":"2303.16214","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-03-28T12:56:54Z","cross_cats_sorted":["quant-ph"],"title_canon_sha256":"e0eb7230bbb197c3d60e007a2bd260405bce8237fd01aa29675d36d19fa7a17c","abstract_canon_sha256":"c316ec280ec9c1a68ff32ce8a4b82ceb8327705df28bb83edabf8714735ba5a8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:13:36.596199Z","signature_b64":"fUziuISlBzhqOWJvh5Ffnc/kQLTBFi5c7gnl9GJ5i0SJCfr9BKn87sZ5ckJChM2il2fXZ0uKrUfunKVLYvUuCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bf020ad00fd7441ca85dad15617545dd7cf20a6c3338674cf3a44d500f8a5d36","last_reissued_at":"2026-07-05T10:13:36.595620Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:13:36.595620Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Tetra-AML: Automatic Machine Learning via Tensor Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["quant-ph"],"primary_cat":"cs.LG","authors_text":"A. Melnikov, A. Naumov, Ar. Melnikov, F. Oxanichenko, K. Izmailov, M. Perelshtein, M. Pflitsch, V. Abronin","submitted_at":"2023-03-28T12:56:54Z","abstract_excerpt":"Neural networks have revolutionized many aspects of society but in the era of huge models with billions of parameters, optimizing and deploying them for commercial applications can require significant computational and financial resources. To address these challenges, we introduce the Tetra-AML toolbox, which automates neural architecture search and hyperparameter optimization via a custom-developed black-box Tensor train Optimization algorithm, TetraOpt. The toolbox also provides model compression through quantization and pruning, augmented by compression using tensor networks. Here, we analy"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.16214","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/2303.16214/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":"2303.16214","created_at":"2026-07-05T10:13:36.595685+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.16214v1","created_at":"2026-07-05T10:13:36.595685+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.16214","created_at":"2026-07-05T10:13:36.595685+00:00"},{"alias_kind":"pith_short_12","alias_value":"X4BAVUAP25CB","created_at":"2026-07-05T10:13:36.595685+00:00"},{"alias_kind":"pith_short_16","alias_value":"X4BAVUAP25CBZKC5","created_at":"2026-07-05T10:13:36.595685+00:00"},{"alias_kind":"pith_short_8","alias_value":"X4BAVUAP","created_at":"2026-07-05T10:13:36.595685+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/X4BAVUAP25CBZKC5VUKWC5KF3V","json":"https://pith.science/pith/X4BAVUAP25CBZKC5VUKWC5KF3V.json","graph_json":"https://pith.science/api/pith-number/X4BAVUAP25CBZKC5VUKWC5KF3V/graph.json","events_json":"https://pith.science/api/pith-number/X4BAVUAP25CBZKC5VUKWC5KF3V/events.json","paper":"https://pith.science/paper/X4BAVUAP"},"agent_actions":{"view_html":"https://pith.science/pith/X4BAVUAP25CBZKC5VUKWC5KF3V","download_json":"https://pith.science/pith/X4BAVUAP25CBZKC5VUKWC5KF3V.json","view_paper":"https://pith.science/paper/X4BAVUAP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.16214&json=true","fetch_graph":"https://pith.science/api/pith-number/X4BAVUAP25CBZKC5VUKWC5KF3V/graph.json","fetch_events":"https://pith.science/api/pith-number/X4BAVUAP25CBZKC5VUKWC5KF3V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/X4BAVUAP25CBZKC5VUKWC5KF3V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/X4BAVUAP25CBZKC5VUKWC5KF3V/action/storage_attestation","attest_author":"https://pith.science/pith/X4BAVUAP25CBZKC5VUKWC5KF3V/action/author_attestation","sign_citation":"https://pith.science/pith/X4BAVUAP25CBZKC5VUKWC5KF3V/action/citation_signature","submit_replication":"https://pith.science/pith/X4BAVUAP25CBZKC5VUKWC5KF3V/action/replication_record"}},"created_at":"2026-07-05T10:13:36.595685+00:00","updated_at":"2026-07-05T10:13:36.595685+00:00"}