Online Class Incremental learning (CIL) is a challenging setting in Cont...
We propose a novel high-fidelity face swapping method called "Arithmetic...
In this work, we examine the advantages of using multiple types of behav...
Generative adversarial networks (GANs) have been being widely used in va...
Approximate inference in deep Bayesian networks exhibits a dilemma of ho...
Analyzing texts from social media encounters many challenges due to thei...
We consider how to effectively use prior knowledge when learning a Bayes...
We consider how to effectively use prior knowledge when learning a Bayes...
Learning hidden topics in data streams has been paid a great deal of
att...
High-dimensional observations and unknown dynamics are major challenges ...
One of the core problems in statistical models is the estimation of a
po...
Topic models are popular for modeling discrete data (e.g., texts, images...
Non-convex optimization problems often arise from probabilistic modeling...
Inference is an integral part of probabilistic topic models, but is ofte...