3 citations · 5 across the 13 of their papers we have counts for
4 papers · 1 filter
Deep Clustering via Distribution Learning
Guanfang Dong, Zijie Tan, Chenqiu Zhao +1
Distribution learning finds probability density functions from a set of data samples, whereas clustering aims to group similar data points to form clusters. Although there are deep…
Bridging Distribution Learning and Image Clustering in High-dimensional Space
Guanfang Dong, Chenqiu Zhao, Anup Basu
Distribution learning focuses on learning the probability density function from a set of data samples. In contrast, clustering aims to group similar objects together in an unsuperv…
Learning Distributions via Monte-Carlo Marginalization
Chenqiu Zhao, Guanfang Dong, Anup Basu
We propose a novel method to learn intractable distributions from their samples. The main idea is to use a parametric distribution model, such as a Gaussian Mixture Model (GMM), to…
Example Perplexity
Nevin L. Zhang, Weiyan Xie, Zhi Lin +4
Some examples are easier for humans to classify than others. The same should be true for deep neural networks (DNNs). We use the term example perplexity to refer to the level of di…