12 citations · 67 across the 40 of their papers we have counts for
3 papers · 1 filter
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…
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…
Semi-Supervised Learning Enabled by Multiscale Deep Neural Network Inversion
Randall Balestriero, Herve Glotin, Richard Baraniuk
Deep Neural Networks (DNNs) provide state-of-the-art solutions in several difficult machine perceptual tasks. However, their performance relies on the availability of a large set o…