Richard Baraniuk

research

∙ 02/02/2023

Unsupervised Learning of Sampling Distributions for Particle Filters

Accurate estimation of the states of a nonlinear dynamical system is cru...

0 Fernando Gama, et al. ∙

research

∙ 10/21/2022

Boomerang: Local sampling on image manifolds using diffusion models

Diffusion models can be viewed as mapping points in a high-dimensional l...

0 Lorenzo Luzi, et al. ∙

research

∙ 08/23/2022

Retrieval-based Controllable Molecule Generation

Generating new molecules with specified chemical and biological properti...

21 Zichao Wang, et al. ∙

research

∙ 05/19/2022

Automated Scoring for Reading Comprehension via In-context BERT Tuning

Automated scoring of open-ended student responses has the potential to s...

0 Nigel Fernandez, et al. ∙

research

∙ 03/15/2022

Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective

We discuss methods for visualizing neural network decision boundaries an...

0 Gowthami Somepalli, et al. ∙

research

∙ 02/16/2022

Spatial Transformer K-Means

K-means defines one of the most employed centroid-based clustering algor...

27 Romain Cosentino, et al. ∙

research

∙ 10/25/2021

Covariate Balancing Methods for Randomized Controlled Trials Are Not Adversarially Robust

The first step towards investigating the effectiveness of a treatment is...

0 Hossein Babaei, et al. ∙

research

∙ 10/15/2021

MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining

Deep Generative Networks (DGNs) are extensively employed in Generative A...

9 Ahmed Imtiaz Humayun, et al. ∙

research

∙ 08/18/2021

Thermal Image Processing via Physics-Inspired Deep Networks

We introduce DeepIR, a new thermal image processing framework that combi...

13 Vishwanath Saragadam, et al. ∙

research

∙ 06/07/2021

Double Descent and Other Interpolation Phenomena in GANs

We study overparameterization in generative adversarial networks (GANs) ...

0 Lorenzo Luzi, et al. ∙

research

∙ 04/25/2021

Math Operation Embeddings for Open-ended Solution Analysis and Feedback

Feedback on student answers and even during intermediate steps in their ...

0 Mengxue Zhang, et al. ∙

research

∙ 04/01/2021

Fast Jacobian-Vector Product for Deep Networks

Jacobian-vector products (JVPs) form the backbone of many recent develop...

0 Randall Balestriero, et al. ∙

research

∙ 01/07/2021

Max-Affine Spline Insights Into Deep Network Pruning

In this paper, we study the importance of pruning in Deep Networks (DNs)...

0 Randall Balestriero, et al. ∙

research

∙ 12/28/2020

SASSI – Super-Pixelated Adaptive Spatio-Spectral Imaging

We introduce a novel video-rate hyperspectral imager with high spatial, ...

3 Vishwanath Saragadam, et al. ∙

research

∙ 12/16/2020

Interpretable Image Clustering via Diffeomorphism-Aware K-Means

We design an interpretable clustering algorithm aware of the nonlinear s...

0 Romain Cosentino, et al. ∙

research

∙ 12/09/2020

Scalable Neural Tangent Kernel of Recurrent Architectures

Kernels derived from deep neural networks (DNNs) in the infinite-width p...

0 Sina Alemohammad, et al. ∙

research

∙ 09/20/2020

Provable Finite Data Generalization with Group Autoencoder

Deep Autoencoders (AEs) provide a versatile framework to learn a compres...

0 Romain Cosentino, et al. ∙

research

∙ 07/07/2020

Computer-Aided Personalized Education

The shortage of people trained in STEM fields is becoming acute, and uni...

0 Rajeev Alur, et al. ∙

research

∙ 06/18/2020

The Recurrent Neural Tangent Kernel

The study of deep networks (DNs) in the infinite-width limit, via the so...

0 Sina Alemohammad, et al. ∙

research

∙ 05/27/2020

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

We propose VarFA, a variational inference factor analysis framework that...

0 Zichao Wang, et al. ∙

research

∙ 02/26/2020

Max-Affine Spline Insights into Deep Generative Networks

We connect a large class of Generative Deep Networks (GDNs) with spline ...

9 Randall Balestriero, et al. ∙

research

∙ 07/10/2019

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

State-of-the-art convolutional neural networks (CNNs) yield record-break...

0 Yue Wang, et al. ∙

research

∙ 05/28/2019

A Hessian Based Complexity Measure for Deep Networks

Deep (neural) networks have been applied productively in a wide range of...

0 Hamid Javadi, et al. ∙

research

∙ 05/21/2019

The Geometry of Deep Networks: Power Diagram Subdivision

We study the geometry of deep (neural) networks (DNs) with piecewise aff...

0 Randall Balestriero, et al. ∙

research

∙ 05/17/2018

A Spline Theory of Deep Networks (Extended Version)

We build a rigorous bridge between deep networks (DNs) and approximation...

6 Randall Balestriero, et al. ∙

research

∙ 02/27/2018

Semi-Supervised Learning Enabled by Multiscale Deep Neural Network Inversion

Deep Neural Networks (DNNs) provide state-of-the-art solutions in severa...

0 Randall Balestriero, et al. ∙

research

∙ 12/25/2017

Overcomplete Frame Thresholding for Acoustic Scene Analysis

In this work, we derive a generic overcomplete frame thresholding scheme...

0 Romain Cosentino, et al. ∙

research

∙ 10/25/2017

Deep Neural Networks

Deep Neural Networks (DNNs) are universal function approximators providi...

0 Randall Balestriero, et al. ∙

research

∙ 09/01/2015

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

FlatCam is a thin form-factor lensless camera that consists of a coded m...

0 M. Salman Asif, et al. ∙

research

∙ 12/01/2014

Fast Sublinear Sparse Representation using Shallow Tree Matching Pursuit

Sparse approximations using highly over-complete dictionaries is a state...

0 Ali Ayremlou, et al. ∙

research

∙ 11/14/2013

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

Compressed sensing enables the reconstruction of high-resolution signals...

0 Tom Goldstein, et al. ∙

Richard Baraniuk

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