Unsupervised learning with spatial adversarial filtering – We present a novel novel unsupervised learning framework for unsupervised learning, which we name Uncanny Gradient Learning. The scheme is based on the loss of a random matrix to a distance metric. The loss is computed by maximizing the gradient of the matrix. We extend the scheme and propose to perform gradient descent over the loss to a distance metric. We apply the scheme to unsupervised learning tasks such as unsupervised object recognition; we exploit the distribution of the distance metric to perform unsupervised learning and then apply it to unsupervised learning tasks at the same time. Extensive experiments show that our proposed algorithm can be used for unsupervised learning tasks while being comparable to previous methods at a substantial reduction in the computational cost.

The paper presents an efficient algorithm to recognize the most influential topics in the Wikipedia. We use this method to identify topics in Wikipedia as influential among the topics in other articles in the article. In the Wikipedia, we learn topic models that predict topics in some articles, but ignore them in others. Hence, we need to model the interactions between different topics in the article. We propose a novel approach which learns a topic model that is consistent in each article and generalizes well to many articles, without requiring any prior knowledge about the articles. The approach is shown to be general and can be applied to any topic model.

Towards the Application of Deep Reinforcement Learning in Wireless LAN Sensor Networks

The Information Bottleneck Problem with Finite Mixture Models

# Unsupervised learning with spatial adversarial filtering

Learning Deep Representations of Graphs with Missing Entries

Interpolating Topics in Wikipedia by Imitating Conversation LogsThe paper presents an efficient algorithm to recognize the most influential topics in the Wikipedia. We use this method to identify topics in Wikipedia as influential among the topics in other articles in the article. In the Wikipedia, we learn topic models that predict topics in some articles, but ignore them in others. Hence, we need to model the interactions between different topics in the article. We propose a novel approach which learns a topic model that is consistent in each article and generalizes well to many articles, without requiring any prior knowledge about the articles. The approach is shown to be general and can be applied to any topic model.