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on language modeling with 5x higher inference throughput.","context_count":1,"top_context_role":"background","top_context_polarity":"unclear","context_text":"In:International Conference on Machine Learning . PMLR. 2017, pp. 3570- 3578. [108] Jue Wang, Wentao Zhu, Pichao Wang, Xiang Yu, Linda Liu, Mohamed Omar, and Raffay Hamid. \"Selective Structured State-Spaces for Long-form Video Understanding\". In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023, pp. 6387-6397. [109] Pete Warden. \"Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition\". In:ArXiv abs/1804.03209 (2018). [110] Samuel Williams, Andrew Waterman, and David Patterson. \"Roofline: An Insightful Visual Performance Model for Multicore Architectures\". In: Communications of the ACM 52.4 (2009), pp. 65-76. [111] Brandon Yang, Gabriel Bender, Quoc V Le, and Jiquan Ngiam."},{"citing_arxiv_id":"2111.00396","ref_index":47,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Efficiently Modeling Long Sequences with Structured State Spaces","primary_cat":"cs.LG","submitted_at":"2021-10-31T03:32:18+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":8.0,"formal_verification":"none","one_line_summary":"S4 is an efficient state space sequence model that captures long-range dependencies via structured parameterization of the SSM, achieving state-of-the-art results on the Long Range Arena and other benchmarks while being faster than Transformers for generation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}