{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:JDRKNH2CVXUNSAV5IJDXII5ZRD","short_pith_number":"pith:JDRKNH2C","canonical_record":{"source":{"id":"1906.00291","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-01T21:09:28Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"b9c3be65e076f9585a4397bd3378a302604a94db0cfad95c39af3ab2d34336c8","abstract_canon_sha256":"0923229609f52bc79109463aa2615bbf8064f7c265ebaff0973d3b998644ab17"},"schema_version":"1.0"},"canonical_sha256":"48e2a69f42ade8d902bd42477423b988f00a0d757fcadf0f1cad904ec92d74fb","source":{"kind":"arxiv","id":"1906.00291","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.00291","created_at":"2026-05-17T23:44:27Z"},{"alias_kind":"arxiv_version","alias_value":"1906.00291v1","created_at":"2026-05-17T23:44:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.00291","created_at":"2026-05-17T23:44:27Z"},{"alias_kind":"pith_short_12","alias_value":"JDRKNH2CVXUN","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"JDRKNH2CVXUNSAV5","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"JDRKNH2C","created_at":"2026-05-18T12:33:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:JDRKNH2CVXUNSAV5IJDXII5ZRD","target":"record","payload":{"canonical_record":{"source":{"id":"1906.00291","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-01T21:09:28Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"b9c3be65e076f9585a4397bd3378a302604a94db0cfad95c39af3ab2d34336c8","abstract_canon_sha256":"0923229609f52bc79109463aa2615bbf8064f7c265ebaff0973d3b998644ab17"},"schema_version":"1.0"},"canonical_sha256":"48e2a69f42ade8d902bd42477423b988f00a0d757fcadf0f1cad904ec92d74fb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:27.560508Z","signature_b64":"jaDoKytHNi8i0R32SchvmOG+Zh0vnmHUu2Wp+ACUI/bOsAKvXrUQAl5y8iFDLauwyArH8CgKifcMjVSLdAuxCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"48e2a69f42ade8d902bd42477423b988f00a0d757fcadf0f1cad904ec92d74fb","last_reissued_at":"2026-05-17T23:44:27.559869Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:27.559869Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.00291","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:44:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"N21R1MzHwteoN9RvLryl3HIoewuBVKgop4FZPVDIuHUJ3YpJIjlfPMgRozNx0XRH2Uduf35OLeV2pYpOAf52Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T02:45:49.874808Z"},"content_sha256":"968c28a6e599db3b23d561de9b443e915e0c618fd9ed7a93c74fccd9da9dfb9b","schema_version":"1.0","event_id":"sha256:968c28a6e599db3b23d561de9b443e915e0c618fd9ed7a93c74fccd9da9dfb9b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:JDRKNH2CVXUNSAV5IJDXII5ZRD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Bob Price, Bo Dai, Eugene Bart, Hanjun Dai, Harsh Shrivastava, Srinivas Aluru","submitted_at":"2019-06-01T21:09:28Z","abstract_excerpt":"We propose a new approach, called cooperative neural networks (CoNN), which uses a set of cooperatively trained neural networks to capture latent representations that exploit prior given independence structure. The model is more flexible than traditional graphical models based on exponential family distributions, but incorporates more domain specific prior structure than traditional deep networks or variational autoencoders. The framework is very general and can be used to exploit the independence structure of any graphical model. We illustrate the technique by showing that we can transfer the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.00291","kind":"arxiv","version":1},"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":"2026-05-17T23:44:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5ZhnmjiOYMNXBx6D+uxTIblxaRhL0Dt3N5ofn1CzRduu5gAkgU57b5z/ezLCPoUvUjvmK5q+LYDXzR6mjEmMCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T02:45:49.875405Z"},"content_sha256":"69fc28544f1d315f8e37371305975d823b543cdb9aa1fac613752ad67cc9d575","schema_version":"1.0","event_id":"sha256:69fc28544f1d315f8e37371305975d823b543cdb9aa1fac613752ad67cc9d575"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JDRKNH2CVXUNSAV5IJDXII5ZRD/bundle.json","state_url":"https://pith.science/pith/JDRKNH2CVXUNSAV5IJDXII5ZRD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JDRKNH2CVXUNSAV5IJDXII5ZRD/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":"2026-05-23T02:45:49Z","links":{"resolver":"https://pith.science/pith/JDRKNH2CVXUNSAV5IJDXII5ZRD","bundle":"https://pith.science/pith/JDRKNH2CVXUNSAV5IJDXII5ZRD/bundle.json","state":"https://pith.science/pith/JDRKNH2CVXUNSAV5IJDXII5ZRD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JDRKNH2CVXUNSAV5IJDXII5ZRD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:JDRKNH2CVXUNSAV5IJDXII5ZRD","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":"0923229609f52bc79109463aa2615bbf8064f7c265ebaff0973d3b998644ab17","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-01T21:09:28Z","title_canon_sha256":"b9c3be65e076f9585a4397bd3378a302604a94db0cfad95c39af3ab2d34336c8"},"schema_version":"1.0","source":{"id":"1906.00291","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.00291","created_at":"2026-05-17T23:44:27Z"},{"alias_kind":"arxiv_version","alias_value":"1906.00291v1","created_at":"2026-05-17T23:44:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.00291","created_at":"2026-05-17T23:44:27Z"},{"alias_kind":"pith_short_12","alias_value":"JDRKNH2CVXUN","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"JDRKNH2CVXUNSAV5","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"JDRKNH2C","created_at":"2026-05-18T12:33:18Z"}],"graph_snapshots":[{"event_id":"sha256:69fc28544f1d315f8e37371305975d823b543cdb9aa1fac613752ad67cc9d575","target":"graph","created_at":"2026-05-17T23:44:27Z","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 propose a new approach, called cooperative neural networks (CoNN), which uses a set of cooperatively trained neural networks to capture latent representations that exploit prior given independence structure. The model is more flexible than traditional graphical models based on exponential family distributions, but incorporates more domain specific prior structure than traditional deep networks or variational autoencoders. The framework is very general and can be used to exploit the independence structure of any graphical model. We illustrate the technique by showing that we can transfer the","authors_text":"Bob Price, Bo Dai, Eugene Bart, Hanjun Dai, Harsh Shrivastava, Srinivas Aluru","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-01T21:09:28Z","title":"Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.00291","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":"sha256:968c28a6e599db3b23d561de9b443e915e0c618fd9ed7a93c74fccd9da9dfb9b","target":"record","created_at":"2026-05-17T23:44:27Z","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":"0923229609f52bc79109463aa2615bbf8064f7c265ebaff0973d3b998644ab17","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-01T21:09:28Z","title_canon_sha256":"b9c3be65e076f9585a4397bd3378a302604a94db0cfad95c39af3ab2d34336c8"},"schema_version":"1.0","source":{"id":"1906.00291","kind":"arxiv","version":1}},"canonical_sha256":"48e2a69f42ade8d902bd42477423b988f00a0d757fcadf0f1cad904ec92d74fb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"48e2a69f42ade8d902bd42477423b988f00a0d757fcadf0f1cad904ec92d74fb","first_computed_at":"2026-05-17T23:44:27.559869Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:44:27.559869Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jaDoKytHNi8i0R32SchvmOG+Zh0vnmHUu2Wp+ACUI/bOsAKvXrUQAl5y8iFDLauwyArH8CgKifcMjVSLdAuxCg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:44:27.560508Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.00291","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:968c28a6e599db3b23d561de9b443e915e0c618fd9ed7a93c74fccd9da9dfb9b","sha256:69fc28544f1d315f8e37371305975d823b543cdb9aa1fac613752ad67cc9d575"],"state_sha256":"b5b32428c45a5a50479cc80a0f1395c4fc81efb444f3d61ecaa43cfe6d2ab9e9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sxF9FMFzmD1DyrlZb2EEv9YCC99v6FMWFufIC/PyLvG+MJlni/nNmUTEJJPXsgTFBsLGeo70wGIbAqLKjirHCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-23T02:45:49.878086Z","bundle_sha256":"cb8157902a48c19f9ce896d3ae7e4e69682267f8ae93b649cbe300c952de3d9a"}}