{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:DRIFLCK6EW2YWKDL5MOD64EUYH","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":"12a2d8175edeca92f120354db1aa3676ab88ff379ffce0fa73b273488c79f26b","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-10-16T07:31:30Z","title_canon_sha256":"a315644c15d1d1a0489e50147c98453ec4cabfd772c9b79fbb4710c8d0b352ba"},"schema_version":"1.0","source":{"id":"2310.10143","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.10143","created_at":"2026-07-05T07:41:43Z"},{"alias_kind":"arxiv_version","alias_value":"2310.10143v2","created_at":"2026-07-05T07:41:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.10143","created_at":"2026-07-05T07:41:43Z"},{"alias_kind":"pith_short_12","alias_value":"DRIFLCK6EW2Y","created_at":"2026-07-05T07:41:43Z"},{"alias_kind":"pith_short_16","alias_value":"DRIFLCK6EW2YWKDL","created_at":"2026-07-05T07:41:43Z"},{"alias_kind":"pith_short_8","alias_value":"DRIFLCK6","created_at":"2026-07-05T07:41:43Z"}],"graph_snapshots":[{"event_id":"sha256:900e2ec11a6d8ec552c6c555a8974be9f8d9a860383afdbf82c7f450e18eb6a5","target":"graph","created_at":"2026-07-05T07:41:43Z","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/2310.10143/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this study, we delve into the problem of self-supervised learning (SSL) utilizing the 1-Wasserstein distance on a tree structure (a.k.a., Tree-Wasserstein distance (TWD)), where TWD is defined as the L1 distance between two tree-embedded vectors. In SSL methods, the cosine similarity is often utilized as an objective function; however, it has not been well studied when utilizing the Wasserstein distance. Training the Wasserstein distance is numerically challenging. Thus, this study empirically investigates a strategy for optimizing the SSL with the Wasserstein distance and finds a stable tr","authors_text":"Deborah Sulem, Guillaume Houry, Han Zhao, Kira Michaela Dusterwald, Makoto Yamada, Yao-Hung Hubert Tsai, Yuki Takezawa","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-10-16T07:31:30Z","title":"An Empirical Study of Self-supervised Learning with Wasserstein Distance"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.10143","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":"sha256:8497c99f4dbeda88ec02b69643135fb4db8408259439b9196bfb7c4c5d1fa799","target":"record","created_at":"2026-07-05T07:41:43Z","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":"12a2d8175edeca92f120354db1aa3676ab88ff379ffce0fa73b273488c79f26b","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-10-16T07:31:30Z","title_canon_sha256":"a315644c15d1d1a0489e50147c98453ec4cabfd772c9b79fbb4710c8d0b352ba"},"schema_version":"1.0","source":{"id":"2310.10143","kind":"arxiv","version":2}},"canonical_sha256":"1c5055895e25b58b286beb1c3f7094c1cd4b63e5270c5d97e7fed1dc0c8501b2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1c5055895e25b58b286beb1c3f7094c1cd4b63e5270c5d97e7fed1dc0c8501b2","first_computed_at":"2026-07-05T07:41:43.773018Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:41:43.773018Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6X2L4CFNlp1CNQ6UIdxVUqZTrU9kMZA0rxxukaV5iEbc8zeuSOPK4vxoJwA5gwxC10guw3l6voGeQFikmMcSAw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:41:43.773517Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.10143","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8497c99f4dbeda88ec02b69643135fb4db8408259439b9196bfb7c4c5d1fa799","sha256:900e2ec11a6d8ec552c6c555a8974be9f8d9a860383afdbf82c7f450e18eb6a5"],"state_sha256":"e9731ca4b4d05b94e892f2d017e91e6be797f078cf8d2f55d43a40b0376c7058"}