This paper describes an unsupervised learning technique for modeling human locomotion styles, such as distinct related activities (e.g. running and striding) or variations of the ...
Many of today's best classification results are obtained by combining the responses of a set of base classifiers to produce an answer for the query. This paper explores a nov...
Standard no-internal-regret (NIR) algorithms compute a fixed point of a matrix, and hence typically require O(n3 ) run time per round of learning, where n is the dimensionality of...
Developing a learning design using IMS Learning Design (LD) is difficult for average practitioners because a high overhead of pedagogical knowledge and technical knowledge is requi...
Yongwu Miao, Tim Sodhi, Francis Brouns, Peter B. S...
We present Policy Gradient Actor-Critic (PGAC), a new model-free Reinforcement Learning (RL) method for creating limited-memory stochastic policies for Partially Observable Markov ...