{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:YIMIHVYDDP3T3BJ3CSZMRDUAZK","short_pith_number":"pith:YIMIHVYD","canonical_record":{"source":{"id":"2103.02405","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-03-03T13:55:12Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"a7ccee34390e39374442d384fe2c9a81148d42d1e59dc8b01da26b59a3a440c8","abstract_canon_sha256":"81afd02083091edb5f135f5b55f655e7eee22cc8fc6079cea80606668b274539"},"schema_version":"1.0"},"canonical_sha256":"c21883d7031bf73d853b14b2c88e80ca9c79b575c9badbc5577d36e9036c9970","source":{"kind":"arxiv","id":"2103.02405","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.02405","created_at":"2026-07-05T02:20:04Z"},{"alias_kind":"arxiv_version","alias_value":"2103.02405v1","created_at":"2026-07-05T02:20:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.02405","created_at":"2026-07-05T02:20:04Z"},{"alias_kind":"pith_short_12","alias_value":"YIMIHVYDDP3T","created_at":"2026-07-05T02:20:04Z"},{"alias_kind":"pith_short_16","alias_value":"YIMIHVYDDP3T3BJ3","created_at":"2026-07-05T02:20:04Z"},{"alias_kind":"pith_short_8","alias_value":"YIMIHVYD","created_at":"2026-07-05T02:20:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:YIMIHVYDDP3T3BJ3CSZMRDUAZK","target":"record","payload":{"canonical_record":{"source":{"id":"2103.02405","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-03-03T13:55:12Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"a7ccee34390e39374442d384fe2c9a81148d42d1e59dc8b01da26b59a3a440c8","abstract_canon_sha256":"81afd02083091edb5f135f5b55f655e7eee22cc8fc6079cea80606668b274539"},"schema_version":"1.0"},"canonical_sha256":"c21883d7031bf73d853b14b2c88e80ca9c79b575c9badbc5577d36e9036c9970","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:20:04.048232Z","signature_b64":"rMkeEczaDQOoB3QzN5trtLpCJvJNsyGUJz+d4trNFhi6fMDRm8xIrlbAH6VQJYsHasWLOQrmi8m2IoTbIk9eDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c21883d7031bf73d853b14b2c88e80ca9c79b575c9badbc5577d36e9036c9970","last_reissued_at":"2026-07-05T02:20:04.047878Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:20:04.047878Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2103.02405","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":"2026-07-05T02:20:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ba6Bw+zuDgS8St4Je8TesFtXid23QyuqdUjsKGMKqzZiXbLGYWWQZpeecS0LJP1mZ59IhEGQGo8joeG9l5bOAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-25T18:00:57.195499Z"},"content_sha256":"d02a626d53f1533aa53c4570c2d2074aa5cee0cbb5764dd8819731e1e2f7c58d","schema_version":"1.0","event_id":"sha256:d02a626d53f1533aa53c4570c2d2074aa5cee0cbb5764dd8819731e1e2f7c58d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:YIMIHVYDDP3T3BJ3CSZMRDUAZK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Relate and Predict: Structure-Aware Prediction with Jointly Optimized Neural DAG","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Arshdeep Sekhon, Yanjun Qi, Zhe Wang","submitted_at":"2021-03-03T13:55:12Z","abstract_excerpt":"Understanding relationships between feature variables is one important way humans use to make decisions. However, state-of-the-art deep learning studies either focus on task-agnostic statistical dependency learning or do not model explicit feature dependencies during prediction. We propose a deep neural network framework, dGAP, to learn neural dependency Graph and optimize structure-Aware target Prediction simultaneously. dGAP trains towards a structure self-supervision loss and a target prediction loss jointly. Our method leads to an interpretable model that can disentangle sparse feature rel"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.02405","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2103.02405/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T02:20:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2JvoYFRcQu0gumAriOBDrNUPsWlzlAjK62nr96Q7K+jMlWkHuSmQOxKnMk+eyl5HMoE4fxKmw3k6SO9WGs02BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-25T18:00:57.195885Z"},"content_sha256":"ba43a25d8c8ad653243f083d163e3de0db751e403d359c35fbff4865bb3dbf4c","schema_version":"1.0","event_id":"sha256:ba43a25d8c8ad653243f083d163e3de0db751e403d359c35fbff4865bb3dbf4c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YIMIHVYDDP3T3BJ3CSZMRDUAZK/bundle.json","state_url":"https://pith.science/pith/YIMIHVYDDP3T3BJ3CSZMRDUAZK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YIMIHVYDDP3T3BJ3CSZMRDUAZK/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":"2026-07-25T18:00:57Z","links":{"resolver":"https://pith.science/pith/YIMIHVYDDP3T3BJ3CSZMRDUAZK","bundle":"https://pith.science/pith/YIMIHVYDDP3T3BJ3CSZMRDUAZK/bundle.json","state":"https://pith.science/pith/YIMIHVYDDP3T3BJ3CSZMRDUAZK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YIMIHVYDDP3T3BJ3CSZMRDUAZK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:YIMIHVYDDP3T3BJ3CSZMRDUAZK","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":"81afd02083091edb5f135f5b55f655e7eee22cc8fc6079cea80606668b274539","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-03-03T13:55:12Z","title_canon_sha256":"a7ccee34390e39374442d384fe2c9a81148d42d1e59dc8b01da26b59a3a440c8"},"schema_version":"1.0","source":{"id":"2103.02405","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.02405","created_at":"2026-07-05T02:20:04Z"},{"alias_kind":"arxiv_version","alias_value":"2103.02405v1","created_at":"2026-07-05T02:20:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.02405","created_at":"2026-07-05T02:20:04Z"},{"alias_kind":"pith_short_12","alias_value":"YIMIHVYDDP3T","created_at":"2026-07-05T02:20:04Z"},{"alias_kind":"pith_short_16","alias_value":"YIMIHVYDDP3T3BJ3","created_at":"2026-07-05T02:20:04Z"},{"alias_kind":"pith_short_8","alias_value":"YIMIHVYD","created_at":"2026-07-05T02:20:04Z"}],"graph_snapshots":[{"event_id":"sha256:ba43a25d8c8ad653243f083d163e3de0db751e403d359c35fbff4865bb3dbf4c","target":"graph","created_at":"2026-07-05T02:20:04Z","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/2103.02405/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Understanding relationships between feature variables is one important way humans use to make decisions. However, state-of-the-art deep learning studies either focus on task-agnostic statistical dependency learning or do not model explicit feature dependencies during prediction. We propose a deep neural network framework, dGAP, to learn neural dependency Graph and optimize structure-Aware target Prediction simultaneously. dGAP trains towards a structure self-supervision loss and a target prediction loss jointly. Our method leads to an interpretable model that can disentangle sparse feature rel","authors_text":"Arshdeep Sekhon, Yanjun Qi, Zhe Wang","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-03-03T13:55:12Z","title":"Relate and Predict: Structure-Aware Prediction with Jointly Optimized Neural DAG"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.02405","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:d02a626d53f1533aa53c4570c2d2074aa5cee0cbb5764dd8819731e1e2f7c58d","target":"record","created_at":"2026-07-05T02:20:04Z","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":"81afd02083091edb5f135f5b55f655e7eee22cc8fc6079cea80606668b274539","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-03-03T13:55:12Z","title_canon_sha256":"a7ccee34390e39374442d384fe2c9a81148d42d1e59dc8b01da26b59a3a440c8"},"schema_version":"1.0","source":{"id":"2103.02405","kind":"arxiv","version":1}},"canonical_sha256":"c21883d7031bf73d853b14b2c88e80ca9c79b575c9badbc5577d36e9036c9970","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c21883d7031bf73d853b14b2c88e80ca9c79b575c9badbc5577d36e9036c9970","first_computed_at":"2026-07-05T02:20:04.047878Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:20:04.047878Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rMkeEczaDQOoB3QzN5trtLpCJvJNsyGUJz+d4trNFhi6fMDRm8xIrlbAH6VQJYsHasWLOQrmi8m2IoTbIk9eDw==","signature_status":"signed_v1","signed_at":"2026-07-05T02:20:04.048232Z","signed_message":"canonical_sha256_bytes"},"source_id":"2103.02405","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d02a626d53f1533aa53c4570c2d2074aa5cee0cbb5764dd8819731e1e2f7c58d","sha256:ba43a25d8c8ad653243f083d163e3de0db751e403d359c35fbff4865bb3dbf4c"],"state_sha256":"36523ee3024d64d1ecb5dd1a7501cb2283d807cd15517a5def5ce0cca538202b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LDHwvJSu6gaj/7O0L2MzI3BcBDDjnRnPgJw2VspDLpvfnjOrxG0q+xXoeHkdybkmRKnahXFrtQjn+W7yHhOcCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-25T18:00:57.198109Z","bundle_sha256":"961842eaad4c761b191288986326cff2707ff39cf135d718b53e5aaf23de56ec"}}