{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:43UJA4YNSEXKRVB2RTTFQEEEJZ","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":"f89f0c4d54c6179455b4edf7044853fb5785417122e1112a6305dd4eb6a9b7f0","cross_cats_sorted":["cs.CL","eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2021-10-15T20:28:27Z","title_canon_sha256":"1eb3c432a439b60a0a7a24d326f1e7b53d6d4a7192a0b4bfaa61ee475420bd7a"},"schema_version":"1.0","source":{"id":"2110.08352","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.08352","created_at":"2026-07-05T04:41:52Z"},{"alias_kind":"arxiv_version","alias_value":"2110.08352v2","created_at":"2026-07-05T04:41:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.08352","created_at":"2026-07-05T04:41:52Z"},{"alias_kind":"pith_short_12","alias_value":"43UJA4YNSEXK","created_at":"2026-07-05T04:41:52Z"},{"alias_kind":"pith_short_16","alias_value":"43UJA4YNSEXKRVB2","created_at":"2026-07-05T04:41:52Z"},{"alias_kind":"pith_short_8","alias_value":"43UJA4YN","created_at":"2026-07-05T04:41:52Z"}],"graph_snapshots":[{"event_id":"sha256:c81756d674cdd80cf49336515ff57171f77ec6f9d2f8c7e9756ed12804d5120c","target":"graph","created_at":"2026-07-05T04:41:52Z","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/2110.08352/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"From wearables to powerful smart devices, modern automatic speech recognition (ASR) models run on a variety of edge devices with different computational budgets. To navigate the Pareto front of model accuracy vs model size, researchers are trapped in a dilemma of optimizing model accuracy by training and fine-tuning models for each individual edge device while keeping the training GPU-hours tractable. In this paper, we propose Omni-sparsity DNN, where a single neural network can be pruned to generate optimized model for a large range of model sizes. We develop training strategies for Omni-spar","authors_text":"Dilin Wang, Ganesh Venkatesh, Haichuan Yang, Meng Li, Ozlem Kalinli, Pierce Chuang, Vikas Chandra, Xiaohui Zhang, Yuan Shangguan","cross_cats":["cs.CL","eess.AS"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2021-10-15T20:28:27Z","title":"Omni-sparsity DNN: Fast Sparsity Optimization for On-Device Streaming E2E ASR via Supernet"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.08352","kind":"arxiv","version":2},"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:f12c372f5c3d3a0a6e3f776c13d8f180d469bc305c1f94e607f5d7a77ab934f3","target":"record","created_at":"2026-07-05T04:41:52Z","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":"f89f0c4d54c6179455b4edf7044853fb5785417122e1112a6305dd4eb6a9b7f0","cross_cats_sorted":["cs.CL","eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2021-10-15T20:28:27Z","title_canon_sha256":"1eb3c432a439b60a0a7a24d326f1e7b53d6d4a7192a0b4bfaa61ee475420bd7a"},"schema_version":"1.0","source":{"id":"2110.08352","kind":"arxiv","version":2}},"canonical_sha256":"e6e890730d912ea8d43a8ce65810844e71f8b889023b4066be663e6edd065120","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e6e890730d912ea8d43a8ce65810844e71f8b889023b4066be663e6edd065120","first_computed_at":"2026-07-05T04:41:52.443145Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:41:52.443145Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9EtGR+2Q9kqIVuvj943s3jVqOP46DhaRCjBXWt4TbMiRojz/Ak0nKQ/T2M21uIPPNibdIxgytJUll9qttro0Ag==","signature_status":"signed_v1","signed_at":"2026-07-05T04:41:52.443553Z","signed_message":"canonical_sha256_bytes"},"source_id":"2110.08352","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f12c372f5c3d3a0a6e3f776c13d8f180d469bc305c1f94e607f5d7a77ab934f3","sha256:c81756d674cdd80cf49336515ff57171f77ec6f9d2f8c7e9756ed12804d5120c"],"state_sha256":"9d7d98a30bec0026e882ae5a196c47251327f2d00f941a17ea7b5fd6dfdda6bc"}