Richard Baraniuk

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Rice University

Provable Finite Data Generalization with Group Autoencoder

Deep Autoencoders (AEs) provide a versatile framework to learn a compres...
09/20/2020 ∙ by Romain Cosentino, et al. ∙ 0 ∙ share

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Computer-Aided Personalized Education

The shortage of people trained in STEM fields is becoming acute, and uni...
07/07/2020 ∙ by Rajeev Alur, et al. ∙ 0 ∙ share

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The Recurrent Neural Tangent Kernel

The study of deep networks (DNs) in the infinite-width limit, via the so...
06/18/2020 ∙ by Sina Alemohammad, et al. ∙ 0 ∙ share

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VarFA: A Variational Factor Analysis Framework For Efficient Bayesian Learning Analytics

We propose VarFA, a variational inference factor analysis framework that...
05/27/2020 ∙ by Zichao Wang, et al. ∙ 0 ∙ share

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Max-Affine Spline Insights into Deep Generative Networks

We connect a large class of Generative Deep Networks (GDNs) with spline ...
02/26/2020 ∙ by Randall Balestriero, et al. ∙ 9 ∙ share

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Dual Dynamic Inference: Enabling More Efficient, Adaptive and Controllable Deep Inference

State-of-the-art convolutional neural networks (CNNs) yield record-break...
07/10/2019 ∙ by Yue Wang, et al. ∙ 0 ∙ share

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A Hessian Based Complexity Measure for Deep Networks

Deep (neural) networks have been applied productively in a wide range of...
05/28/2019 ∙ by Hamid Javadi, et al. ∙ 0 ∙ share

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The Geometry of Deep Networks: Power Diagram Subdivision

We study the geometry of deep (neural) networks (DNs) with piecewise aff...
05/21/2019 ∙ by Randall Balestriero, et al. ∙ 0 ∙ share

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A Spline Theory of Deep Networks (Extended Version)

We build a rigorous bridge between deep networks (DNs) and approximation...
05/17/2018 ∙ by Randall Balestriero, et al. ∙ 6 ∙ share

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Semi-Supervised Learning Enabled by Multiscale Deep Neural Network Inversion

Deep Neural Networks (DNNs) provide state-of-the-art solutions in severa...
02/27/2018 ∙ by Randall Balestriero, et al. ∙ 0 ∙ share

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Overcomplete Frame Thresholding for Acoustic Scene Analysis

In this work, we derive a generic overcomplete frame thresholding scheme...
12/25/2017 ∙ by Romain Cosentino, et al. ∙ 0 ∙ share

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Deep Neural Networks

Deep Neural Networks (DNNs) are universal function approximators providi...
10/25/2017 ∙ by Randall Balestriero, et al. ∙ 0 ∙ share

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FlatCam: Thin, Bare-Sensor Cameras using Coded Aperture and Computation

FlatCam is a thin form-factor lensless camera that consists of a coded m...
09/01/2015 ∙ by M. Salman Asif, et al. ∙ 0 ∙ share

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Fast Sublinear Sparse Representation using Shallow Tree Matching Pursuit

Sparse approximations using highly over-complete dictionaries is a state...
12/01/2014 ∙ by Ali Ayremlou, et al. ∙ 0 ∙ share

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The STONE Transform: Multi-Resolution Image Enhancement and Real-Time Compressive Video

Compressed sensing enables the reconstruction of high-resolution signals...
11/14/2013 ∙ by Tom Goldstein, et al. ∙ 0 ∙ share

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Richard Baraniuk

Featured Co-authors

Yue Wang

Zhangyang Wang

Tom Goldstein

Armando Solar-Lezama

Pengfei Xu

Randall Balestriero

Ashok Veeraraghavan

Yingyan Lin

M. Salman Asif

Herve Glotin

Sumit Gulwani

Provable Finite Data Generalization with Group Autoencoder

Computer-Aided Personalized Education

The Recurrent Neural Tangent Kernel

VarFA: A Variational Factor Analysis Framework For Efficient Bayesian Learning Analytics

Max-Affine Spline Insights into Deep Generative Networks

Dual Dynamic Inference: Enabling More Efficient, Adaptive and Controllable Deep Inference

A Hessian Based Complexity Measure for Deep Networks

The Geometry of Deep Networks: Power Diagram Subdivision

A Spline Theory of Deep Networks (Extended Version)

Semi-Supervised Learning Enabled by Multiscale Deep Neural Network Inversion

Overcomplete Frame Thresholding for Acoustic Scene Analysis

Deep Neural Networks

FlatCam: Thin, Bare-Sensor Cameras using Coded Aperture and Computation

Fast Sublinear Sparse Representation using Shallow Tree Matching Pursuit

The STONE Transform: Multi-Resolution Image Enhancement and Real-Time Compressive Video