{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:GQXJK2EJVUQBFSP452MCBYOICO","short_pith_number":"pith:GQXJK2EJ","schema_version":"1.0","canonical_sha256":"342e956889ad2012c9fcee9820e1c813a8d2e5523227ddbb623fb5f33bb2a422","source":{"kind":"arxiv","id":"2201.09874","version":1},"attestation_state":"computed","paper":{"title":"CVAE-H: Conditionalizing Variational Autoencoders via Hypernetworks and Trajectory Forecasting for Autonomous Driving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.RO"],"primary_cat":"cs.LG","authors_text":"Geunseob Oh, Huei Peng","submitted_at":"2022-01-24T18:50:39Z","abstract_excerpt":"The task of predicting stochastic behaviors of road agents in diverse environments is a challenging problem for autonomous driving. To best understand scene contexts and produce diverse possible future states of the road agents adaptively in different environments, a prediction model should be probabilistic, multi-modal, context-driven, and general. We present Conditionalizing Variational AutoEncoders via Hypernetworks (CVAE-H); a conditional VAE that extensively leverages hypernetwork and performs generative tasks for high-dimensional problems like the prediction task. We first evaluate CVAE-"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2201.09874","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-01-24T18:50:39Z","cross_cats_sorted":["cs.AI","cs.RO"],"title_canon_sha256":"5037b877ba15352504072acccd483a28b0c54e02fc571172e277c03d7aeb7261","abstract_canon_sha256":"3a64cb433a92fdc424a579cabbca413acd90cd52bc88f250922a04c40589fbca"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:51:02.072466Z","signature_b64":"X1dPTcrsmUzLVrg0gSMuNvMOXx3K1CDoDpj67G5FYwfCqcQ9zhsSoBbGG83EiP5tZtlvhGANYBiwojIUVGGOBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"342e956889ad2012c9fcee9820e1c813a8d2e5523227ddbb623fb5f33bb2a422","last_reissued_at":"2026-07-05T03:51:02.071978Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:51:02.071978Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CVAE-H: Conditionalizing Variational Autoencoders via Hypernetworks and Trajectory Forecasting for Autonomous Driving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.RO"],"primary_cat":"cs.LG","authors_text":"Geunseob Oh, Huei Peng","submitted_at":"2022-01-24T18:50:39Z","abstract_excerpt":"The task of predicting stochastic behaviors of road agents in diverse environments is a challenging problem for autonomous driving. To best understand scene contexts and produce diverse possible future states of the road agents adaptively in different environments, a prediction model should be probabilistic, multi-modal, context-driven, and general. We present Conditionalizing Variational AutoEncoders via Hypernetworks (CVAE-H); a conditional VAE that extensively leverages hypernetwork and performs generative tasks for high-dimensional problems like the prediction task. We first evaluate CVAE-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.09874","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/2201.09874/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2201.09874","created_at":"2026-07-05T03:51:02.072037+00:00"},{"alias_kind":"arxiv_version","alias_value":"2201.09874v1","created_at":"2026-07-05T03:51:02.072037+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.09874","created_at":"2026-07-05T03:51:02.072037+00:00"},{"alias_kind":"pith_short_12","alias_value":"GQXJK2EJVUQB","created_at":"2026-07-05T03:51:02.072037+00:00"},{"alias_kind":"pith_short_16","alias_value":"GQXJK2EJVUQBFSP4","created_at":"2026-07-05T03:51:02.072037+00:00"},{"alias_kind":"pith_short_8","alias_value":"GQXJK2EJ","created_at":"2026-07-05T03:51:02.072037+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.00761","citing_title":"Learning to Forget using Hypernetworks","ref_index":36,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GQXJK2EJVUQBFSP452MCBYOICO","json":"https://pith.science/pith/GQXJK2EJVUQBFSP452MCBYOICO.json","graph_json":"https://pith.science/api/pith-number/GQXJK2EJVUQBFSP452MCBYOICO/graph.json","events_json":"https://pith.science/api/pith-number/GQXJK2EJVUQBFSP452MCBYOICO/events.json","paper":"https://pith.science/paper/GQXJK2EJ"},"agent_actions":{"view_html":"https://pith.science/pith/GQXJK2EJVUQBFSP452MCBYOICO","download_json":"https://pith.science/pith/GQXJK2EJVUQBFSP452MCBYOICO.json","view_paper":"https://pith.science/paper/GQXJK2EJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2201.09874&json=true","fetch_graph":"https://pith.science/api/pith-number/GQXJK2EJVUQBFSP452MCBYOICO/graph.json","fetch_events":"https://pith.science/api/pith-number/GQXJK2EJVUQBFSP452MCBYOICO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GQXJK2EJVUQBFSP452MCBYOICO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GQXJK2EJVUQBFSP452MCBYOICO/action/storage_attestation","attest_author":"https://pith.science/pith/GQXJK2EJVUQBFSP452MCBYOICO/action/author_attestation","sign_citation":"https://pith.science/pith/GQXJK2EJVUQBFSP452MCBYOICO/action/citation_signature","submit_replication":"https://pith.science/pith/GQXJK2EJVUQBFSP452MCBYOICO/action/replication_record"}},"created_at":"2026-07-05T03:51:02.072037+00:00","updated_at":"2026-07-05T03:51:02.072037+00:00"}