12 citations · 41 across the 18 of their papers we have counts for
24 papers
Batch Normalization Explained
Randall Balestriero, Richard G. Baraniuk
A critically important, ubiquitous, and yet poorly understood ingredient in modern deep networks (DNs) is batch normalization (BN), which centers and normalizes the feature maps. T…
DeepTensor: Low-Rank Tensor Decomposition with Deep Network Priors
Vishwanath Saragadam, Randall Balestriero, Ashok Veeraraghavan +1
DeepTensor is a computationally efficient framework for low-rank decomposition of matrices and tensors using deep generative networks. We decompose a tensor as the product of low-r…
Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values
Ahmed Imtiaz Humayun, Randall Balestriero, Richard Baraniuk
We present Polarity Sampling, a theoretically justified plug-and-play method for controlling the generation quality and diversity of pre-trained deep generative networks DGNs). Lev…
NeuroView-RNN: It's About Time
CJ Barberan, Sina Alemohammad, Naiming Liu +2
Recurrent Neural Networks (RNNs) are important tools for processing sequential data such as time-series or video. Interpretability is defined as the ability to be understood by a p…
Spatial Transformer K-Means
Romain Cosentino, Randall Balestriero, Yanis Bahroun +3
K-means defines one of the most employed centroid-based clustering algorithms with performances tied to the data's embedding. Intricate data embeddings have been designed to push $…
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…