Abstract. Bayesian reinforcement learning (RL) is aimed at making more efficient use of data samples, but typically uses significantly more computation. For discrete Markov Decis...
Large data resources are ubiquitous in science and business. For these domains, an intuitive view on the data is essential to fully exploit the hidden knowledge. Often, these data...
This paper introduces a new definition of dense subgraph pattern, the DN-graph. DN-graph considers both the size of the sub-structure and the minimum level of interactions betwee...
Nan Wang, Jingbo Zhang, Kian-Lee Tan, Anthony K. H...
Abstract--The Affinity Propagation (AP) clustering algorithm proposed by Frey and Dueck (2007) provides an understandable, nearly optimal summary of a data set. However, it suffers...
On large datasets, the popular training approach has been stochastic gradient descent (SGD). This paper proposes a modification of SGD, called averaged SGD with feedback (ASF), tha...