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A Bayesian Perspective of Convolutional Neural Networks through a Deconvolutional Generative Model
Tan Nguyen, Nhat Ho, Ankit Patel +3
Inspired by the success of Convolutional Neural Networks (CNNs) for supervised prediction in images, we design the Deconvolutional Generative Model (DGM), a new probabilistic gener…
From Hard to Soft: Understanding Deep Network Nonlinearities via Vector Quantization and Statistical Inference
Randall Balestriero, Richard G. Baraniuk
Nonlinearity is crucial to the performance of a deep (neural) network (DN). To date there has been little progress understanding the menagerie of available nonlinearities, but rece…
An Expectation-Maximization Approach to Tuning Generalized Vector Approximate Message Passing
Christopher A. Metzler, Philip Schniter, Richard G. Baraniuk
Generalized Vector Approximate Message Passing (GVAMP) is an efficient iterative algorithm for approximately minimum-mean-squared-error estimation of a random vector $\mathbf{x}\si…
MISSION: Ultra Large-Scale Feature Selection using Count-Sketches
Amirali Aghazadeh, Ryan Spring, Daniel LeJeune +3
Feature selection is an important challenge in machine learning. It plays a crucial role in the explainability of machine-driven decisions that are rapidly permeating throughout mo…
Unsupervised Learning with Stein's Unbiased Risk Estimator
Christopher A. Metzler, Ali Mousavi, Reinhard Heckel +1
Learning from unlabeled and noisy data is one of the grand challenges of machine learning. As such, it has seen a flurry of research with new ideas proposed continuously. In this w…
Mad Max: Affine Spline Insights into Deep Learning
Randall Balestriero, Richard Baraniuk
We build a rigorous bridge between deep networks (DNs) and approximation theory via spline functions and operators. Our key result is that a large class of DNs can be written as a…