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07/07/2023
MultiQG-TI: Towards Question Generation from Multi-modal Sources
We study the new problem of automatic question generation (QG) from mult...
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02/02/2023
Unsupervised Learning of Sampling Distributions for Particle Filters
Accurate estimation of the states of a nonlinear dynamical system is cru...
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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...
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08/23/2022
Retrieval-based Controllable Molecule Generation
Generating new molecules with specified chemical and biological properti...
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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...
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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...
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02/16/2022
Spatial Transformer K-Means
K-means defines one of the most employed centroid-based clustering algor...
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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...
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10/15/2021
MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining
Deep Generative Networks (DGNs) are extensively employed in Generative A...
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08/18/2021
Thermal Image Processing via Physics-Inspired Deep Networks
We introduce DeepIR, a new thermal image processing framework that combi...
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06/07/2021
Double Descent and Other Interpolation Phenomena in GANs
We study overparameterization in generative adversarial networks (GANs) ...
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04/25/2021
Math Operation Embeddings for Open-ended Solution Analysis and Feedback
Feedback on student answers and even during intermediate steps in their ...
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04/01/2021
Fast Jacobian-Vector Product for Deep Networks
Jacobian-vector products (JVPs) form the backbone of many recent develop...
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01/07/2021
Max-Affine Spline Insights Into Deep Network Pruning
In this paper, we study the importance of pruning in Deep Networks (DNs)...
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12/28/2020
SASSI – Super-Pixelated Adaptive Spatio-Spectral Imaging
We introduce a novel video-rate hyperspectral imager with high spatial, ...
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12/16/2020
Interpretable Image Clustering via Diffeomorphism-Aware K-Means
We design an interpretable clustering algorithm aware of the nonlinear s...
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12/09/2020
Scalable Neural Tangent Kernel of Recurrent Architectures
Kernels derived from deep neural networks (DNNs) in the infinite-width p...
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09/20/2020
Provable Finite Data Generalization with Group Autoencoder
Deep Autoencoders (AEs) provide a versatile framework to learn a compres...
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07/07/2020
Computer-Aided Personalized Education
The shortage of people trained in STEM fields is becoming acute, and uni...
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06/18/2020
The Recurrent Neural Tangent Kernel
The study of deep networks (DNs) in the infinite-width limit, via the so...
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05/27/2020
VarFA: A Variational Factor Analysis Framework For Efficient Bayesian Learning Analytics
We propose VarFA, a variational inference factor analysis framework that...
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02/26/2020
Max-Affine Spline Insights into Deep Generative Networks
We connect a large class of Generative Deep Networks (GDNs) with spline ...
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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...
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05/28/2019
A Hessian Based Complexity Measure for Deep Networks
Deep (neural) networks have been applied productively in a wide range of...
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05/21/2019
The Geometry of Deep Networks: Power Diagram Subdivision
We study the geometry of deep (neural) networks (DNs) with piecewise aff...
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05/17/2018
A Spline Theory of Deep Networks (Extended Version)
We build a rigorous bridge between deep networks (DNs) and approximation...
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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...
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12/25/2017
Overcomplete Frame Thresholding for Acoustic Scene Analysis
In this work, we derive a generic overcomplete frame thresholding scheme...
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10/25/2017
Deep Neural Networks
Deep Neural Networks (DNNs) are universal function approximators providi...
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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...
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12/01/2014
Fast Sublinear Sparse Representation using Shallow Tree Matching Pursuit
Sparse approximations using highly over-complete dictionaries is a state...
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11/14/2013