{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:LL7IGJWHW57VY2NFKQL2P27EXZ","short_pith_number":"pith:LL7IGJWH","canonical_record":{"source":{"id":"1701.07875","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-01-26T21:10:29Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"bd38f21cbd051c7eda544845da455f631d5f031dfb721acc5e8ee74317adb51b","abstract_canon_sha256":"55b03dae9c5726700a606ab6970f8cbe521e0e37df9d3246a097ea99d7181db8"},"schema_version":"1.0"},"canonical_sha256":"5afe8326c7b77f5c69a55417a7ebe4be4a0246ac429933a42eaa6e5a15e309ea","source":{"kind":"arxiv","id":"1701.07875","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1701.07875","created_at":"2026-05-18T00:28:35Z"},{"alias_kind":"arxiv_version","alias_value":"1701.07875v3","created_at":"2026-05-18T00:28:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.07875","created_at":"2026-05-18T00:28:35Z"},{"alias_kind":"pith_short_12","alias_value":"LL7IGJWHW57V","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_16","alias_value":"LL7IGJWHW57VY2NF","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_8","alias_value":"LL7IGJWH","created_at":"2026-05-18T12:31:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:LL7IGJWHW57VY2NFKQL2P27EXZ","target":"record","payload":{"canonical_record":{"source":{"id":"1701.07875","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-01-26T21:10:29Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"bd38f21cbd051c7eda544845da455f631d5f031dfb721acc5e8ee74317adb51b","abstract_canon_sha256":"55b03dae9c5726700a606ab6970f8cbe521e0e37df9d3246a097ea99d7181db8"},"schema_version":"1.0"},"canonical_sha256":"5afe8326c7b77f5c69a55417a7ebe4be4a0246ac429933a42eaa6e5a15e309ea","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:28:35.727628Z","signature_b64":"2pRByeb0foNY6moRJbte/HZozGoXDbAsZ/fgJLYJWpvK1X1uz9aEzs+vvcM0lh61sj80xu64YjkS2Pfc4J4NAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5afe8326c7b77f5c69a55417a7ebe4be4a0246ac429933a42eaa6e5a15e309ea","last_reissued_at":"2026-05-18T00:28:35.727014Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:28:35.727014Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1701.07875","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:28:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lHf1I+lywREPK2GyCdZ0GAe1kg/ix/DOYwpOtl0BvqwUfDBnFZnhVTKXZ1Jnx6ewMiDegRv4IT6YEU/SD3BtBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T11:57:02.119241Z"},"content_sha256":"77d5075f7336332237c5d826e1e4d4923ab135519a5cf83c2f82f2b53ca258bd","schema_version":"1.0","event_id":"sha256:77d5075f7336332237c5d826e1e4d4923ab135519a5cf83c2f82f2b53ca258bd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:LL7IGJWHW57VY2NFKQL2P27EXZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Wasserstein GAN","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"L\\'eon Bottou, Martin Arjovsky, Soumith Chintala","submitted_at":"2017-01-26T21:10:29Z","abstract_excerpt":"We introduce a new algorithm named WGAN, an alternative to traditional GAN training. In this new model, we show that we can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches. Furthermore, we show that the corresponding optimization problem is sound, and provide extensive theoretical work highlighting the deep connections to other distances between distributions."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.07875","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:28:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gMI1wvhVcoFrhIsGTChnZMnLiTvhDJwCD3qjKJB9efL7hlOuI4AcyAzB5tpV9bWmPfjpb9tdica3ldWS06vTCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T11:57:02.119611Z"},"content_sha256":"af66f32e7291e9ca416782c8738bbdd5cd90c1ea5f42d9e308dbdb8d7adc35b8","schema_version":"1.0","event_id":"sha256:af66f32e7291e9ca416782c8738bbdd5cd90c1ea5f42d9e308dbdb8d7adc35b8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LL7IGJWHW57VY2NFKQL2P27EXZ/bundle.json","state_url":"https://pith.science/pith/LL7IGJWHW57VY2NFKQL2P27EXZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LL7IGJWHW57VY2NFKQL2P27EXZ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-05T11:57:02Z","links":{"resolver":"https://pith.science/pith/LL7IGJWHW57VY2NFKQL2P27EXZ","bundle":"https://pith.science/pith/LL7IGJWHW57VY2NFKQL2P27EXZ/bundle.json","state":"https://pith.science/pith/LL7IGJWHW57VY2NFKQL2P27EXZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LL7IGJWHW57VY2NFKQL2P27EXZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:LL7IGJWHW57VY2NFKQL2P27EXZ","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":"55b03dae9c5726700a606ab6970f8cbe521e0e37df9d3246a097ea99d7181db8","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-01-26T21:10:29Z","title_canon_sha256":"bd38f21cbd051c7eda544845da455f631d5f031dfb721acc5e8ee74317adb51b"},"schema_version":"1.0","source":{"id":"1701.07875","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1701.07875","created_at":"2026-05-18T00:28:35Z"},{"alias_kind":"arxiv_version","alias_value":"1701.07875v3","created_at":"2026-05-18T00:28:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.07875","created_at":"2026-05-18T00:28:35Z"},{"alias_kind":"pith_short_12","alias_value":"LL7IGJWHW57V","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_16","alias_value":"LL7IGJWHW57VY2NF","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_8","alias_value":"LL7IGJWH","created_at":"2026-05-18T12:31:28Z"}],"graph_snapshots":[{"event_id":"sha256:af66f32e7291e9ca416782c8738bbdd5cd90c1ea5f42d9e308dbdb8d7adc35b8","target":"graph","created_at":"2026-05-18T00:28:35Z","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"},"paper":{"abstract_excerpt":"We introduce a new algorithm named WGAN, an alternative to traditional GAN training. In this new model, we show that we can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches. Furthermore, we show that the corresponding optimization problem is sound, and provide extensive theoretical work highlighting the deep connections to other distances between distributions.","authors_text":"L\\'eon Bottou, Martin Arjovsky, Soumith Chintala","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-01-26T21:10:29Z","title":"Wasserstein GAN"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.07875","kind":"arxiv","version":3},"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":"sha256:77d5075f7336332237c5d826e1e4d4923ab135519a5cf83c2f82f2b53ca258bd","target":"record","created_at":"2026-05-18T00:28:35Z","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":"55b03dae9c5726700a606ab6970f8cbe521e0e37df9d3246a097ea99d7181db8","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-01-26T21:10:29Z","title_canon_sha256":"bd38f21cbd051c7eda544845da455f631d5f031dfb721acc5e8ee74317adb51b"},"schema_version":"1.0","source":{"id":"1701.07875","kind":"arxiv","version":3}},"canonical_sha256":"5afe8326c7b77f5c69a55417a7ebe4be4a0246ac429933a42eaa6e5a15e309ea","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5afe8326c7b77f5c69a55417a7ebe4be4a0246ac429933a42eaa6e5a15e309ea","first_computed_at":"2026-05-18T00:28:35.727014Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:28:35.727014Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2pRByeb0foNY6moRJbte/HZozGoXDbAsZ/fgJLYJWpvK1X1uz9aEzs+vvcM0lh61sj80xu64YjkS2Pfc4J4NAA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:28:35.727628Z","signed_message":"canonical_sha256_bytes"},"source_id":"1701.07875","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:77d5075f7336332237c5d826e1e4d4923ab135519a5cf83c2f82f2b53ca258bd","sha256:af66f32e7291e9ca416782c8738bbdd5cd90c1ea5f42d9e308dbdb8d7adc35b8"],"state_sha256":"9754832dcb20a07163a2e20ddd48776c993d5459564f25d0f3bae23f3a00ca73"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"N8ZmYmLHUyF9WzpN+N+c5AqI8snU3syoiwxQuoYxURf6gEjPL1xQ1PicoVXz3xMvT9V+bW6CiA4I4TnbiWJCCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T11:57:02.121560Z","bundle_sha256":"eab0b01169101e9b9a706c7c2d3b527c5fa51a4d74c5affffc8c26d75f4c09c0"}}