{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:NJMNRMSI6RFTZJ2TH2UGNSC3ID","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":"3215c62ad63dccd523d69d35509b5fa5b04a2130be30069307e1d625e592b391","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-19T18:25:22Z","title_canon_sha256":"ba7f2529ddcdb4ad15e2d68797369d11fd1147302c79ad7c141a9cd06de7c86d"},"schema_version":"1.0","source":{"id":"2310.13085","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.13085","created_at":"2026-07-05T07:02:56Z"},{"alias_kind":"arxiv_version","alias_value":"2310.13085v1","created_at":"2026-07-05T07:02:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.13085","created_at":"2026-07-05T07:02:56Z"},{"alias_kind":"pith_short_12","alias_value":"NJMNRMSI6RFT","created_at":"2026-07-05T07:02:56Z"},{"alias_kind":"pith_short_16","alias_value":"NJMNRMSI6RFTZJ2T","created_at":"2026-07-05T07:02:56Z"},{"alias_kind":"pith_short_8","alias_value":"NJMNRMSI","created_at":"2026-07-05T07:02:56Z"}],"graph_snapshots":[{"event_id":"sha256:09654f2d7c1c08e6c2b97e2885cf922275ef17a5606a18681ecc6a361ebfacbb","target":"graph","created_at":"2026-07-05T07:02:56Z","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/2310.13085/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Few-shot learning or meta-learning leverages the data scarcity problem in machine learning. Traditionally, training data requires a multitude of samples and labeling for supervised learning. To address this issue, we propose a one-shot unsupervised meta-learning to learn the latent representation of the training samples. We use augmented samples as the query set during the training phase of the unsupervised meta-learning. A temperature-scaled cross-entropy loss is used in the inner loop of meta-learning to prevent overfitting during unsupervised learning. The learned parameters from this step ","authors_text":"Atik Faysal, Avimanyu Sahoo, Huaxia Wang, Mohammad Rostami, Ryan Antle","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-19T18:25:22Z","title":"Unsupervised Representation Learning to Aid Semi-Supervised Meta Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.13085","kind":"arxiv","version":1},"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:f41692b57a51092a476c52b00d7007b6c057c94524d5a67533ae2ee7665147ce","target":"record","created_at":"2026-07-05T07:02:56Z","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":"3215c62ad63dccd523d69d35509b5fa5b04a2130be30069307e1d625e592b391","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-19T18:25:22Z","title_canon_sha256":"ba7f2529ddcdb4ad15e2d68797369d11fd1147302c79ad7c141a9cd06de7c86d"},"schema_version":"1.0","source":{"id":"2310.13085","kind":"arxiv","version":1}},"canonical_sha256":"6a58d8b248f44b3ca7533ea866c85b40cca64e983385ce70ea6a7f76b1c77ef1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6a58d8b248f44b3ca7533ea866c85b40cca64e983385ce70ea6a7f76b1c77ef1","first_computed_at":"2026-07-05T07:02:56.980198Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:02:56.980198Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TkxfkRxXPVBDwMNZnYagFdv/2llpKxnbLE9j5trn8B/FH4OuxFQ4rrpPLgpExdbNhcFWNFAoL7YVxyvoJ98mCw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:02:56.980625Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.13085","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f41692b57a51092a476c52b00d7007b6c057c94524d5a67533ae2ee7665147ce","sha256:09654f2d7c1c08e6c2b97e2885cf922275ef17a5606a18681ecc6a361ebfacbb"],"state_sha256":"fdaf37f6fa594abe8cbb5f6c6e05c25192e8b3b9d750cb802b36cc4e7b17a9f6"}