Exploration–Exploitation Mechanisms in Recurrent Neural Networks and Human Learners in Restless Bandit Problemsdoi:10.1007/s42113-024-00202-yExploration-exploitation trade-offRecurrent neural networksComputational modeling of behaviorMeta-reinforcement learning...
On completion of this educational activity, learners should be better able to: (1) Appropriately counsel patients after an episode of uncomplicated diverti... D Beddy,B Wolff - 《Journal of Clinical Gastroenterology》 被引量: 4发表: 2011年 加载更多来源...
Recurrent neural networks, long short-term memory, and gated recurrent units were utilized to predict the concentration of e-learners. A set of comparative experiments was conducted. As a result, gated recurrent units exhibited the best performance. The main contribution of this paper is to ...
Meta-learningStackingARIMARNNLSTMTime series forecasting is a problem that is strongly dependent on the underlying process which generates the data sequence. Hence,finding good model fits often involves complex and time consuming tasks such as extensive data preprocessing, designing hybrid models, or ...
Recurrent neural network models (RNNs) have recently gained traction in both human and systems neuroscience work on reinforcement learning, due to their ability to show meta-learning of task domains. Here we compre- hensively compared the performance of a range of RNN architectures as well as ...
Low engagement indicates that the student is not engaged in the learning content or there is a clear indication of engagement in other non-learning content; engagement shows that learners are involved in the learning process, but this involvement is limited and susceptible to interruption; high ...