
Deep Generative Models that Solve PDEs: Distributed Computing for Training Large DataFree Models
Recent progress in scientific machine learning (SciML) has opened up the...
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Hyperparameter Optimization in Neural Networks via Structured Sparse Recovery
In this paper, we study two important problems in the automated design o...
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ESPN: Extremely Sparse Pruned Networks
Deep neural networks are often highly overparameterized, prohibiting the...
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Benefits of Jointly Training Autoencoders: An Improved Neural Tangent Kernel Analysis
A remarkable recent discovery in machine learning has been that deep neu...
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On Higherorder Moments in Adam
In this paper, we investigate the popular deep learning optimization rou...
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Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents
Robustness of Deep Reinforcement Learning (DRL) algorithms towards adver...
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Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors
Deep neural networks as image priors have been recently introduced for p...
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OneShot Neural Architecture Search via Compressive Sensing
Neural architecture search (NAS), or automated design of neural network ...
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Encoding Invariances in Deep Generative Models
Reliable training of generative adversarial networks (GANs) typically re...
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Reducing The Search Space For Hyperparameter Optimization Using Group Sparsity
We propose a new algorithm for hyperparameter selection in machine learn...
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Semantic Adversarial Attacks: Parametric Transformations That Fool Deep Classifiers
Deep neural networks have been shown to exhibit an intriguing vulnerabil...
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A Kaczmarz Algorithm for Solving Tree Based Distributed Systems of Equations
The Kaczmarz algorithm is an iterative method for solving systems of lin...
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Alternating Phase Projected Gradient Descent with Generative Priors for Solving Compressive Phase Retrieval
The classical problem of phase retrieval arises in various signal acquis...
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Signal Reconstruction from Modulo Observations
We consider the problem of reconstructing a signal from underdetermined...
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Physicsaware Deep Generative Models for Creating Synthetic Microstructures
A key problem in computational material science deals with understanding...
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Algorithmic Aspects of Inverse Problems Using Generative Models
The traditional approach of handcrafting priors (such as sparsity) for ...
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Phase Retrieval for Signals in Union of Subspaces
We consider the phase retrieval problem for signals that belong to a uni...
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Learning ReLU Networks via Alternating Minimization
We propose and analyze a new family of algorithms for training neural ne...
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Autoencoders Learn Generative Linear Models
Recent progress in learning theory has led to the emergence of provable ...
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On ConsensusOptimality Tradeoffs in Collaborative Deep Learning
In distributed machine learning, where agents collaboratively learn from...
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On Learning Sparsely Used Dictionaries from Incomplete Samples
Most existing algorithms for dictionary learning assume that all entries...
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Solving Linear Inverse Problems Using GAN Priors: An Algorithm with Provable Guarantees
In recent works, both sparsitybased methods as well as learningbased m...
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Fast LowRank Matrix Estimation without the Condition Number
In this paper, we study the general problem of optimizing a convex funct...
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A ForwardBackward Approach for Visualizing Information Flow in Deep Networks
We introduce a new, systematic framework for visualizing information flo...
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Provably Accurate DoubleSparse Coding
Sparse coding is a crucial subroutine in algorithms for various signal p...
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Reconstruction from Periodic Nonlinearities, With Applications to HDR Imaging
We consider the problem of reconstructing signals and images from period...
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Demixing Structured Superposition Signals from Periodic and Aperiodic Nonlinear Observations
We consider the demixing problem of two (or more) structured highdimens...
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Fast Algorithms for Learning Latent Variables in Graphical Models
We study the problem of learning latent variables in Gaussian graphical ...
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Collaborative Deep Learning in Fixed Topology Networks
There is significant recent interest to parallelize deep learning algori...
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Improved Algorithms for Matrix Recovery from RankOne Projections
We consider the problem of estimation of a lowrank matrix from a limite...
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SampleEfficient Algorithms for Recovering Structured Signals from MagnitudeOnly Measurements
We consider the problem of recovering a signal x^* ∈R^n, from magnitude...
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Stable Recovery Of Sparse Vectors From Random Sinusoidal Feature Maps
Random sinusoidal features are a popular approach for speeding up kernel...
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Iterative Thresholding for Demixing Structured Superpositions in High Dimensions
We consider the demixing problem of two (or more) highdimensional vecto...
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Fast Algorithms for Demixing Sparse Signals from Nonlinear Observations
We study the problem of demixing a pair of sparse signals from noisy, no...
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Efficient Upsampling of Natural Images
We propose a novel method of efficient upsampling of a single natural im...
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Signal Recovery on Incoherent Manifolds
Suppose that we observe noisy linear measurements of an unknown signal t...
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A Theoretical Analysis of Joint Manifolds
The emergence of lowcost sensor architectures for diverse modalities ha...
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Chinmay Hegde
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Assistant Professor at New York University