{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:23YBNW35RY66ZCUZETKAMLFXMU","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":"09bb1011f1e96dc322d4ba0a07bc84a99a847c3518b1ed34c3c015873671c6a3","cross_cats_sorted":["cs.AI","cs.NE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-31T09:03:57Z","title_canon_sha256":"7ee463b912fe468ec4b868090e3f5ce265d5209409ea0554530843e9ef659f23"},"schema_version":"1.0","source":{"id":"2205.15619","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.15619","created_at":"2026-07-05T05:40:06Z"},{"alias_kind":"arxiv_version","alias_value":"2205.15619v2","created_at":"2026-07-05T05:40:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.15619","created_at":"2026-07-05T05:40:06Z"},{"alias_kind":"pith_short_12","alias_value":"23YBNW35RY66","created_at":"2026-07-05T05:40:06Z"},{"alias_kind":"pith_short_16","alias_value":"23YBNW35RY66ZCUZ","created_at":"2026-07-05T05:40:06Z"},{"alias_kind":"pith_short_8","alias_value":"23YBNW35","created_at":"2026-07-05T05:40:06Z"}],"graph_snapshots":[{"event_id":"sha256:a00cac66b093b635a6999c07e70136941eed51db7711feb03b94857baf790daf","target":"graph","created_at":"2026-07-05T05:40:06Z","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/2205.15619/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Few-shot learning for neural networks (NNs) is an important problem that aims to train NNs with a few data. The main challenge is how to avoid overfitting since over-parameterized NNs can easily overfit to such small dataset. Previous work (e.g. MAML by Finn et al. 2017) tackles this challenge by meta-learning, which learns how to learn from a few data by using various tasks. On the other hand, one conventional approach to avoid overfitting is restricting hypothesis spaces by endowing sparse NN structures like convolution layers in computer vision. However, although such manually-designed spar","authors_text":"Atsutoshi Kumagai, Daiki Chijiwa, Shin'ya Yamaguchi, Yasutoshi Ida","cross_cats":["cs.AI","cs.NE","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-31T09:03:57Z","title":"Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.15619","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":"sha256:0b0871b7c5bef8baabc4f2b4e467d9e028465f626e15aa9f9efc7177624ffdfc","target":"record","created_at":"2026-07-05T05:40:06Z","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":"09bb1011f1e96dc322d4ba0a07bc84a99a847c3518b1ed34c3c015873671c6a3","cross_cats_sorted":["cs.AI","cs.NE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-31T09:03:57Z","title_canon_sha256":"7ee463b912fe468ec4b868090e3f5ce265d5209409ea0554530843e9ef659f23"},"schema_version":"1.0","source":{"id":"2205.15619","kind":"arxiv","version":2}},"canonical_sha256":"d6f016db7d8e3dec8a9924d4062cb7652f9abe788daf8793f2503a1101eeb004","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d6f016db7d8e3dec8a9924d4062cb7652f9abe788daf8793f2503a1101eeb004","first_computed_at":"2026-07-05T05:40:06.854129Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:40:06.854129Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"K5OUvKWW3aH9TNKZvdzBPSa1hrnT+glwT1Q+ccYqenujmaaDpbsxYHlkV/FrSnBYkEaNSzI0JDlWw1KR7NqfBg==","signature_status":"signed_v1","signed_at":"2026-07-05T05:40:06.854621Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.15619","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0b0871b7c5bef8baabc4f2b4e467d9e028465f626e15aa9f9efc7177624ffdfc","sha256:a00cac66b093b635a6999c07e70136941eed51db7711feb03b94857baf790daf"],"state_sha256":"cefed611ac61918882238b248789f1e062bc7d58e0c4f43613a79fcbc95625f9"}