{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:57KDTA5JPUFN25Q6Q46IO4TVAO","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":"e25e9944b863a3b092c4a384b6c2fe66c3e59ceb13bcb7f6cae5dbbdd6eb32e5","cross_cats_sorted":["cs.SD"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2020-05-21T06:07:30Z","title_canon_sha256":"04b5f376853ec1f7b440183f922076a910769d6c616767a0737cb710c7b31430"},"schema_version":"1.0","source":{"id":"2005.11172","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2005.11172","created_at":"2026-07-05T01:05:05Z"},{"alias_kind":"arxiv_version","alias_value":"2005.11172v1","created_at":"2026-07-05T01:05:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.11172","created_at":"2026-07-05T01:05:05Z"},{"alias_kind":"pith_short_12","alias_value":"57KDTA5JPUFN","created_at":"2026-07-05T01:05:05Z"},{"alias_kind":"pith_short_16","alias_value":"57KDTA5JPUFN25Q6","created_at":"2026-07-05T01:05:05Z"},{"alias_kind":"pith_short_8","alias_value":"57KDTA5J","created_at":"2026-07-05T01:05:05Z"}],"graph_snapshots":[{"event_id":"sha256:c79f3c26d7a91579e7c11bea6cad493ff073899bcc1cf0c4647e78895952e226","target":"graph","created_at":"2026-07-05T01:05:05Z","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/2005.11172/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep reinforcement learning (deep RL) is a combination of deep learning with reinforcement learning principles to create efficient methods that can learn by interacting with its environment. This has led to breakthroughs in many complex tasks, such as playing the game \"Go\", that were previously difficult to solve. However, deep RL requires significant training time making it difficult to use in various real-life applications such as Human-Computer Interaction (HCI). In this paper, we study pre-training in deep RL to reduce the training time and improve the performance of Speech Recognition, a ","authors_text":"Bj\\\"orn W. Schuller, Rajib Rana, Sara Khalifa, Siddique Latif, Thejan Rajapakshe","cross_cats":["cs.SD"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2020-05-21T06:07:30Z","title":"Deep Reinforcement Learning with Pre-training for Time-efficient Training of Automatic Speech Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.11172","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:ae4d479c913bf05b16c53d08b444b0b0e657568e3181aaabe581a3547d8f6b10","target":"record","created_at":"2026-07-05T01:05:05Z","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":"e25e9944b863a3b092c4a384b6c2fe66c3e59ceb13bcb7f6cae5dbbdd6eb32e5","cross_cats_sorted":["cs.SD"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2020-05-21T06:07:30Z","title_canon_sha256":"04b5f376853ec1f7b440183f922076a910769d6c616767a0737cb710c7b31430"},"schema_version":"1.0","source":{"id":"2005.11172","kind":"arxiv","version":1}},"canonical_sha256":"efd43983a97d0add761e873c87727503a86674a8c71964e01cd3a721492903ea","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"efd43983a97d0add761e873c87727503a86674a8c71964e01cd3a721492903ea","first_computed_at":"2026-07-05T01:05:05.975686Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:05:05.975686Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cwOMJc+rwXLzyktCMtNJXnI2w8RdO83q8M+1mJEKJG75ZafyuvNSoHTKIPUKIT7HBvDa0NmRiY0Lsuvv9g9FAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T01:05:05.976110Z","signed_message":"canonical_sha256_bytes"},"source_id":"2005.11172","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ae4d479c913bf05b16c53d08b444b0b0e657568e3181aaabe581a3547d8f6b10","sha256:c79f3c26d7a91579e7c11bea6cad493ff073899bcc1cf0c4647e78895952e226"],"state_sha256":"9a8a09d26d7ef500e19109b0da737b2599f9607169cb1c39cb456ee91d25c863"}