A comprehensive artificial intelligence system needs to not only perceive the environment
with different “senses”(eg, seeing and hearing) but also infer the world's conditional (or even …

Topic modeling analyzes documents to learn meaningful patterns of words. However,
existing topic models fail to learn interpretable topics when working with large and heavy …

In recent years, studies related to topic derivation in Twitter have gained a lot of interest from
businesses and academics. The interconnection between users and information has made …

Topic modeling analyzes documents to learn meaningful patterns of words. For documents
collected in sequence, dynamic topic models capture how these patterns vary over time. We …

To train an inference network jointly with a deep generative topic model, making it both
scalable to big corpora and fast in out-of-sample prediction, we develop Weibull hybrid …

Recent work on variational autoencoders (VAEs) has enabled the development of
generative topic models using neural networks. Topic models based on latent Dirichlet …

Hierarchical topic models such as the gamma belief network (GBN) have delivered
promising results in mining multi-layer document representations and discovering …

Clustering Web services can improve the quality and efficiency of service discovery and
management within a service repository. Nowadays, Web services frequently interact (eg …

We develop a recurrent gamma belief network (rGBN) for radar automatic target recognition
(RATR) based on high-resolution range profile (HRRP), which characterizes the temporal …

Automated service classification plays a crucial role in service discovery, selection, and
composition. Machine learning has been widely used for service classification in recent …