Many modern unsupervised or semi-supervised machine learning algorithms rely on
Bayesian probabilistic models. These models are usually intractable and thus require …

Machine learning is a field at the intersection of statistics and computer science that uses
algorithms to extract information and knowledge from data. Its applications increasingly find …

We propose a new regularization method based on virtual adversarial loss: a new measure
of local smoothness of the conditional label distribution given input. Virtual adversarial loss …

Topic modeling is one of the most powerful techniques in text mining for data mining, latent
data discovery, and finding relationships among data and text documents. Researchers …

Recently, multi-view representation learning has become a rapidly growing direction in
machine learning and data mining areas. This paper introduces two categories for multi …

Robust low-level image features have been proven to be effective representations for a
variety of visual recognition tasks such as object recognition and scene classification; but …

This paper investigates the problem of modeling Internet images and associated text or tags
for tasks such as image-to-image search, tag-to-image search, and image-to-tag search …

This work presents a new approach to learning a frame-based classifier on weakly labelled
sequence data by embedding a CNN within an iterative EM algorithm. This allows the CNN …

In this article, we review probabilistic topic models: graphical models that can be used to
summarize a large collection of documents with a smaller number of distributions over …

The problem of describing images through natural language has gained importance in the
computer vision community. Solutions to image description have either focused on a top …