{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:OTIMNFZCKV7Y4RCWP6NNWMAD2S","short_pith_number":"pith:OTIMNFZC","canonical_record":{"source":{"id":"1706.03850","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-06-12T20:55:51Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"57368f2df2b5bb228f78ce8f1e089dcb03485ab25088ccd4db57b7e77e0c4060","abstract_canon_sha256":"39e6672231fc67ed81f79b9cfeca6771a617b1649d1d3cb6f0fd0ae6e9ce5e60"},"schema_version":"1.0"},"canonical_sha256":"74d0c69722557f8e44567f9adb3003d491d25e1c2b5c9d7fb076cf9c17d48ea1","source":{"kind":"arxiv","id":"1706.03850","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.03850","created_at":"2026-05-18T00:30:16Z"},{"alias_kind":"arxiv_version","alias_value":"1706.03850v3","created_at":"2026-05-18T00:30:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.03850","created_at":"2026-05-18T00:30:16Z"},{"alias_kind":"pith_short_12","alias_value":"OTIMNFZCKV7Y","created_at":"2026-05-18T12:31:34Z"},{"alias_kind":"pith_short_16","alias_value":"OTIMNFZCKV7Y4RCW","created_at":"2026-05-18T12:31:34Z"},{"alias_kind":"pith_short_8","alias_value":"OTIMNFZC","created_at":"2026-05-18T12:31:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:OTIMNFZCKV7Y4RCWP6NNWMAD2S","target":"record","payload":{"canonical_record":{"source":{"id":"1706.03850","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-06-12T20:55:51Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"57368f2df2b5bb228f78ce8f1e089dcb03485ab25088ccd4db57b7e77e0c4060","abstract_canon_sha256":"39e6672231fc67ed81f79b9cfeca6771a617b1649d1d3cb6f0fd0ae6e9ce5e60"},"schema_version":"1.0"},"canonical_sha256":"74d0c69722557f8e44567f9adb3003d491d25e1c2b5c9d7fb076cf9c17d48ea1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:30:16.732594Z","signature_b64":"333IX37htZIpsWybRR/ZJn7kNRBG4Tqvbfmh1mLg1Mgv1e14X4vN+mV0ZB2gI2JQ2tdPnPp7zOoQTmQCxU0kCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"74d0c69722557f8e44567f9adb3003d491d25e1c2b5c9d7fb076cf9c17d48ea1","last_reissued_at":"2026-05-18T00:30:16.731985Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:30:16.731985Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.03850","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":"2026-05-18T00:30:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6l1jSZsPY4aUZfahFW/uOzB/nEoS0wXeHEvHCLHt6QDHfPnEn6loO+P/Kw/vMT9ASd2+f7pMr48AYadxetU2Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-19T02:45:18.944104Z"},"content_sha256":"5a9f6cc50a9d52072d6dfc620dc3407accad68821a47b89908ace95c67292959","schema_version":"1.0","event_id":"sha256:5a9f6cc50a9d52072d6dfc620dc3407accad68821a47b89908ace95c67292959"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:OTIMNFZCKV7Y4RCWP6NNWMAD2S","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Adversarial Feature Matching for Text Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"stat.ML","authors_text":"Dinghan Shen, Kai Fan, Lawrence Carin, Ricardo Henao, Yizhe Zhang, Zhe Gan, Zhi Chen","submitted_at":"2017-06-12T20:55:51Z","abstract_excerpt":"The Generative Adversarial Network (GAN) has achieved great success in generating realistic (real-valued) synthetic data. However, convergence issues and difficulties dealing with discrete data hinder the applicability of GAN to text. We propose a framework for generating realistic text via adversarial training. We employ a long short-term memory network as generator, and a convolutional network as discriminator. Instead of using the standard objective of GAN, we propose matching the high-dimensional latent feature distributions of real and synthetic sentences, via a kernelized discrepancy met"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.03850","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":"2026-05-18T00:30:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"T08raFw2Zl+5U+6N+hzmRpzhl7mdGiOxmjMyMZPU+aLbf3qGMuJN1NM9NbBL/B0JYHmGjRAvbE34JTZpHC9vDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-19T02:45:18.944483Z"},"content_sha256":"f01a0102f2726e5a83616422227508e6ebaeee70baf50ec9172442599370b473","schema_version":"1.0","event_id":"sha256:f01a0102f2726e5a83616422227508e6ebaeee70baf50ec9172442599370b473"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OTIMNFZCKV7Y4RCWP6NNWMAD2S/bundle.json","state_url":"https://pith.science/pith/OTIMNFZCKV7Y4RCWP6NNWMAD2S/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OTIMNFZCKV7Y4RCWP6NNWMAD2S/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":"2026-05-19T02:45:18Z","links":{"resolver":"https://pith.science/pith/OTIMNFZCKV7Y4RCWP6NNWMAD2S","bundle":"https://pith.science/pith/OTIMNFZCKV7Y4RCWP6NNWMAD2S/bundle.json","state":"https://pith.science/pith/OTIMNFZCKV7Y4RCWP6NNWMAD2S/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OTIMNFZCKV7Y4RCWP6NNWMAD2S/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:OTIMNFZCKV7Y4RCWP6NNWMAD2S","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":"39e6672231fc67ed81f79b9cfeca6771a617b1649d1d3cb6f0fd0ae6e9ce5e60","cross_cats_sorted":["cs.CL","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-06-12T20:55:51Z","title_canon_sha256":"57368f2df2b5bb228f78ce8f1e089dcb03485ab25088ccd4db57b7e77e0c4060"},"schema_version":"1.0","source":{"id":"1706.03850","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.03850","created_at":"2026-05-18T00:30:16Z"},{"alias_kind":"arxiv_version","alias_value":"1706.03850v3","created_at":"2026-05-18T00:30:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.03850","created_at":"2026-05-18T00:30:16Z"},{"alias_kind":"pith_short_12","alias_value":"OTIMNFZCKV7Y","created_at":"2026-05-18T12:31:34Z"},{"alias_kind":"pith_short_16","alias_value":"OTIMNFZCKV7Y4RCW","created_at":"2026-05-18T12:31:34Z"},{"alias_kind":"pith_short_8","alias_value":"OTIMNFZC","created_at":"2026-05-18T12:31:34Z"}],"graph_snapshots":[{"event_id":"sha256:f01a0102f2726e5a83616422227508e6ebaeee70baf50ec9172442599370b473","target":"graph","created_at":"2026-05-18T00:30:16Z","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":"The Generative Adversarial Network (GAN) has achieved great success in generating realistic (real-valued) synthetic data. However, convergence issues and difficulties dealing with discrete data hinder the applicability of GAN to text. We propose a framework for generating realistic text via adversarial training. We employ a long short-term memory network as generator, and a convolutional network as discriminator. Instead of using the standard objective of GAN, we propose matching the high-dimensional latent feature distributions of real and synthetic sentences, via a kernelized discrepancy met","authors_text":"Dinghan Shen, Kai Fan, Lawrence Carin, Ricardo Henao, Yizhe Zhang, Zhe Gan, Zhi Chen","cross_cats":["cs.CL","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-06-12T20:55:51Z","title":"Adversarial Feature Matching for Text Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.03850","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":"sha256:5a9f6cc50a9d52072d6dfc620dc3407accad68821a47b89908ace95c67292959","target":"record","created_at":"2026-05-18T00:30:16Z","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":"39e6672231fc67ed81f79b9cfeca6771a617b1649d1d3cb6f0fd0ae6e9ce5e60","cross_cats_sorted":["cs.CL","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-06-12T20:55:51Z","title_canon_sha256":"57368f2df2b5bb228f78ce8f1e089dcb03485ab25088ccd4db57b7e77e0c4060"},"schema_version":"1.0","source":{"id":"1706.03850","kind":"arxiv","version":3}},"canonical_sha256":"74d0c69722557f8e44567f9adb3003d491d25e1c2b5c9d7fb076cf9c17d48ea1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"74d0c69722557f8e44567f9adb3003d491d25e1c2b5c9d7fb076cf9c17d48ea1","first_computed_at":"2026-05-18T00:30:16.731985Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:30:16.731985Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"333IX37htZIpsWybRR/ZJn7kNRBG4Tqvbfmh1mLg1Mgv1e14X4vN+mV0ZB2gI2JQ2tdPnPp7zOoQTmQCxU0kCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:30:16.732594Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.03850","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5a9f6cc50a9d52072d6dfc620dc3407accad68821a47b89908ace95c67292959","sha256:f01a0102f2726e5a83616422227508e6ebaeee70baf50ec9172442599370b473"],"state_sha256":"66c7fe0011b45fa73f1b9cd1509f8c54741c146b925d19eb000375e41953df1c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0EjJfIy8tFnnRVGySINZW9hiywO/w6G5sDFL4PhvwonqvqODSTVCH04bKq5p5QAigPcQAcZeruBmTCk0dDOyAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-19T02:45:18.946052Z","bundle_sha256":"fcf4e2062103e76362e851c7f6f2bf8b4a83343aa84d3b10ff5c6c879be5985c"}}