Distributed learning is a problem of fundamental interest in machine learning and cognitive science. In this paper, we present asynchronous distributed learning algorithms for two...
We present a computational framework to automatically discover high-order temporal social patterns from very noisy and sparse location data. We introduce the concept of social foo...
Language model (LM) adaptation is important for both speech and language processing. It is often achieved by combining a generic LM with a topic-specific model that is more releva...
In this paper, we propose a new method, Parametric Embedding (PE), for visualizing the posteriors estimated over a mixture model. PE simultaneously embeds both objects and their c...
Tomoharu Iwata, Kazumi Saito, Naonori Ueda, Sean S...
Topic modeling has been a key problem for document analysis. One of the canonical approaches for topic modeling is Probabilistic Latent Semantic Indexing, which maximizes the join...
Deng Cai, Qiaozhu Mei, Jiawei Han, Chengxiang Zhai