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Found 7 papers, 1 papers with code
Wide Compression: Tensor Ring Nets
Deep neural networks have demonstrated state-of-the-art performance in a variety of real-world applications.
Deep Unsupervised Clustering Using Mixture of Autoencoders
Unsupervised clustering is one of the most fundamental challenges in machine learning.
Block CUR: Decomposing Matrices using Groups of Columns
In this paper, we consider matrix approximation by sampling predefined \emph{blocks} of columns (or rows) from the matrix.
A Compressed Sensing Based Decomposition of Electrodermal Activity Signals
The measurement and analysis of Electrodermal Activity (EDA) offers applications in diverse areas ranging from market research, to seizure detection, to human stress analysis.
Learning Latent Variable Gaussian Graphical Models
Gaussian graphical models (GGM) have been widely used in many high-dimensional applications ranging from biological and financial data to recommender systems.
Matroid Bandits: Fast Combinatorial Optimization with Learning
The objective in these problems is to learn how to maximize a modular function on a matroid.
Adaptive Submodular Maximization in Bandit Setting
Maximization of submodular functions has wide applications in machine learning and artificial intelligence.
