1.3k citations · 4.9k across the 150 of their papers we have counts for
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NeuroView: Explainable Deep Network Decision Making
CJ Barberan, Randall Balestriero, Richard G. Baraniuk
Deep neural networks (DNs) provide superhuman performance in numerous computer vision tasks, yet it remains unclear exactly which of a DN's units contribute to a particular decisio…
NFT-K: Non-Fungible Tangent Kernels
Sina Alemohammad, Hossein Babaei, CJ Barberan +4
Deep neural networks have become essential for numerous applications due to their strong empirical performance such as vision, RL, and classification. Unfortunately, these networks…
Unrolling Particles: Unsupervised Learning of Sampling Distributions
Fernando Gama, Nicolas Zilberstein, Richard G. Baraniuk +1
Particle filtering is used to compute good nonlinear estimates of complex systems. It samples trajectories from a chosen distribution and computes the estimate as a weighted averag…
Covariate Balancing Methods for Randomized Controlled Trials Are Not Adversarially Robust
Hossein Babaei, Sina Alemohammad, Richard Baraniuk
The first step towards investigating the effectiveness of a treatment via a randomized trial is to split the population into control and treatment groups then compare the average r…
Improving Transformers with Probabilistic Attention Keys
Tam Nguyen, Tan M. Nguyen, Dung D. Le +5
Multi-head attention is a driving force behind state-of-the-art transformers, which achieve remarkable performance across a variety of natural language processing (NLP) and compute…
MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining
Ahmed Imtiaz Humayun, Randall Balestriero, Richard Baraniuk
Deep Generative Networks (DGNs) are extensively employed in Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and their variants to approximate the data mani…