Results 71 to 80 of about 1,052,376 (334)
Deep Reinforcement Learning for NLP [PDF]
Proceedings of ACL 2018, Tutorial Abstracts, 2018 Many Natural Language Processing (NLP) tasks (including generation, language grounding, reasoning, information extraction, coreference resolution, and dialog) can be formulated as deep reinforcement learning (DRL) problems. However, since language is often discrete and the space for all sentences is infinite, there are many challenges for formulating ...
William Yang Wang +2 more
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Kickstarting Deep Reinforcement Learning
CoRR, 2018 We present a method for using previously-trained 'teacher' agents to kickstart the training of a new 'student' agent. To this end, we leverage ideas from policy distillation and population based training. Our method places no constraints on the architecture of the teacher or student agents, and it regulates itself to allow the students to surpass their
Simon Schmitt +10 more
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Routing algorithms as tools for integrating social distancing with emergency evacuation
Scientific Reports, 2021 One of the lessons from the COVID-19 pandemic is the importance of social distancing, even in challenging circumstances such as pre-hurricane evacuation. To explore the implications of integrating social distancing with evacuation operations, we describe
Yi-Lin Tsai +3 more
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Deep Ordinal Reinforcement Learning [PDF]
, 2020 replaced figures for better visibility, added github repository, more details about source of experimental results, updated target value calculation for standard and ordinal Deep Q ...
Alexander Zap +2 more
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Improvement of Deep Reinforcement Learning Using Curriculum in Game Environment
مجله پژوهش در علوم توانبخشی, 2020 Introduction: Training deep curriculum learning is a kind of smart agent training in which, first the simple acts, and then, the difficult acts are trained to smart agent.
Mohammadreza Mohammadnejad +2 more
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Deep reinforcement learning for imbalanced classification [PDF]
Applied Intelligence, 2020 Data in real-world application often exhibit skewed class distribution which poses an intense challenge for machine learning. Conventional classification algorithms are not effective in the case of imbalanced data distribution, and may fail when the data distribution is highly imbalanced.
Enlu Lin, Qiong Chen, Xiaoming Qi
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Collaborative Deep Reinforcement Learning
CoRR, 2017 Besides independent learning, human learning process is highly improved by summarizing what has been learned, communicating it with peers, and subsequently fusing knowledge from different sources to assist the current learning goal. This collaborative learning procedure ensures that the knowledge is shared, continuously refined, and concluded from ...
Kaixiang Lin, Shu Wang, Jiayu Zhou
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A Survey for Deep Reinforcement Learning Based Network Intrusion Detection
Applied AI LettersCyber‐attacks are gradually becoming more sophisticated and highly frequent nowadays, and the significance of network intrusion detection systems has become more pronounced.
Wanrong Yang +3 more
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FEBS Open Bio, EarlyView.Activation of the mitochondrial protein OXR1 increases pSyn129 αSynuclein aggregation by lowering ATP levels and altering mitochondrial membrane potential, particularly in response to MSA‐derived fibrils. In contrast, ablation of the ER protein EMC4 enhances autophagic flux and lysosomal clearance, broadly reducing α‐synuclein aggregates.
Sandesh Neupane +11 more
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EnergiesRecent breakthroughs in artificial intelligence are accelerating the intelligent transformation of vehicles. Vehicle electronic and electrical architectures are converging toward centralized domain controllers.
Dagang Lu +11 more
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