76 citations · 258 across the 8 of their papers we have counts for
9 papers
Wide Compression: Tensor Ring Nets
Wenqi Wang, Yifan Sun, Brian Eriksson +2
Deep neural networks have demonstrated state-of-the-art performance in a variety of real-world applications. In order to obtain performance gains, these networks have grown larger…
Deep Unsupervised Clustering Using Mixture of Autoencoders
Dejiao Zhang, Yifan Sun, Brian Eriksson +1
Unsupervised clustering is one of the most fundamental challenges in machine learning. A popular hypothesis is that data are generated from a union of low-dimensional nonlinear man…
Block CUR: Decomposing Matrices using Groups of Columns
Urvashi Oswal, Swayambhoo Jain, Kevin S. Xu +1
A common problem in large-scale data analysis is to approximate a matrix using a combination of specifically sampled rows and columns, known as CUR decomposition. Unfortunately, in…
A Compressed Sensing Based Decomposition of Electrodermal Activity Signals
Swayambhoo Jain, Urvashi Oswal, Kevin S. Xu +2
The measurement and analysis of Electrodermal Activity (EDA) offers applications in diverse areas ranging from market research, to seizure detection, to human stress analysis. Unfo…
Learning Latent Variable Gaussian Graphical Models
Zhaoshi Meng, Brian Eriksson, Alfred O. Hero
Gaussian graphical models (GGM) have been widely used in many high-dimensional applications ranging from biological and financial data to recommender systems. Sparsity in GGM plays…
Matroid Bandits: Fast Combinatorial Optimization with Learning
Branislav Kveton, Zheng Wen, Azin Ashkan +2
A matroid is a notion of independence in combinatorial optimization which is closely related to computational efficiency. In particular, it is well known that the maximum of a cons…