20 citations · 26 across the 9 of their papers we have counts for
10 papers
Exploiting Exact Conditionals Improves Conditioning: Provably Fast Mixing Time Bounds By Sampling from the Marginal
Abhijit Chowdhary, Federica Milinanni, Julianne Chung +1
The problem of sampling from a probability distribution arises in many applications such as posterior sampling in hierarchical Bayesian inverse problems and Gaussian processes for…
Boost Like a (Var)Pro: Trust-Region Gradient Boosting via Variable Projection
Abhijit Chowdhary, Elizabeth Newman, Deepanshu Verma
Gradient boosting, a method of building additive ensembles from weak learners, has established itself as a practical and theoretically-motivated approach to approximate functions,…
Tensor-Tensor Products, Group Representations, and Semidefinite Programming
Alex Dunbar, Elizabeth Newman
The -family of tensor-tensor products is a framework which generalizes many properties from linear algebra to third order tensors. Here, we investigate positive semidefini…
Image reconstructions using sparse dictionary representations and implicit, non-negative mappings
Elizabeth Newman, Jack Michael Solomon, Matthias Chung
Many imaging science tasks can be modeled as a discrete linear inverse problem. Solving linear inverse problems is often challenging, with ill-conditioned operators and potentially…
slimTrain -- A Stochastic Approximation Method for Training Separable Deep Neural Networks
Elizabeth Newman, Julianne Chung, Matthias Chung +1
Deep neural networks (DNNs) have shown their success as high-dimensional function approximators in many applications; however, training DNNs can be challenging in general. DNN trai…
Train Like a (Var)Pro: Efficient Training of Neural Networks with Variable Projection
Elizabeth Newman, Lars Ruthotto, Joseph Hart +1
Deep neural networks (DNNs) have achieved state-of-the-art performance across a variety of traditional machine learning tasks, e.g., speech recognition, image classification, and s…
Tensor-Tensor Products for Optimal Representation and Compression
Misha Kilmer, Lior Horesh, Haim Avron +1
In this era of big data, data analytics and machine learning, it is imperative to find ways to compress large data sets such that intrinsic features necessary for subsequent analys…
Non-negative Tensor Patch Dictionary Approaches for Image Compression and Deblurring Applications
Elizabeth Newman, Misha E. Kilmer
In recent work (Soltani, Kilmer, Hansen, BIT 2016), an algorithm for non-negative tensor patch dictionary learning in the context of X-ray CT imaging and based on a tensor-tensor p…
Stable Tensor Neural Networks for Rapid Deep Learning
Elizabeth Newman, Lior Horesh, Haim Avron +1
We propose a tensor neural network (-NN) framework that offers an exciting new paradigm for designing neural networks with multidimensional (tensor) data. Our network architectu…
Image classification using local tensor singular value decompositions
Elizabeth Newman, Misha Kilmer, Lior Horesh
From linear classifiers to neural networks, image classification has been a widely explored topic in mathematics, and many algorithms have proven to be effective classifiers. Howev…