1.3k citations · 4.9k across the 150 of their papers we have counts for
6 papers · 2 filters
Semi-Supervised Learning via New Deep Network Inversion
Randall Balestriero, Vincent Roger, Herve G. Glotin +1
We exploit a recently derived inversion scheme for arbitrary deep neural networks to develop a new semi-supervised learning framework that applies to a wide range of systems and pr…
Deep Neural Networks
Randall Balestriero, Richard Baraniuk
Deep Neural Networks (DNNs) are universal function approximators providing state-of- the-art solutions on wide range of applications. Common perceptual tasks such as speech recogni…
DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural Networks
Ali Mousavi, Gautam Dasarathy, Richard G. Baraniuk
In this paper we develop a novel computational sensing framework for sensing and recovering structured signals. When trained on a set of representative signals, our framework learn…
Learned D-AMP: Principled Neural Network based Compressive Image Recovery
Christopher A. Metzler, Ali Mousavi, Richard G. Baraniuk
Compressive image recovery is a challenging problem that requires fast and accurate algorithms. Recently, neural networks have been applied to this problem with promising results.…
Data-Mining Textual Responses to Uncover Misconception Patterns
Joshua J. Michalenko, Andrew S. Lan, Richard G. Baraniuk
An important, yet largely unstudied, problem in student data analysis is to detect misconceptions from students' responses to open-response questions. Misconception detection enabl…
Learning to Invert: Signal Recovery via Deep Convolutional Networks
Ali Mousavi, Richard G. Baraniuk
The promise of compressive sensing (CS) has been offset by two significant challenges. First, real-world data is not exactly sparse in a fixed basis. Second, current high-performan…