Chinmay Hegde

发表

NuMax: A Convex Approach for Learning Near-Isometric Linear Embeddings

Chinmay Hegde, Aswin C. Sankaranarayanan, Wotao Yin, 2015, IEEE Transactions on Signal Processing.

Random Projections for Manifold Learning

Chinmay Hegde, Richard G. Baraniuk, Michael B. Wakin, 2007, NIPS.

Nearly optimal linear embeddings into very low dimensions

Piotr Indyk, Chinmay Hegde, Elyot Grant, 2013, 2013 IEEE Global Conference on Signal and Information Processing.

Compressive sensing of a superposition of pulses

Chinmay Hegde, Richard G. Baraniuk, Richard Baraniuk, 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

Object Tracking via Compressive Sensing

Chinmay Hegde, Yun Li, Kevin F. Kelly, 2014 .

Automatic fault localization using the generalized Earth Mover's distance

Piotr Indyk, Chinmay Hegde, Ludwig Schmidt, 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

LIE operators for compressive sensing

Chinmay Hegde, Aswin C. Sankaranarayanan, Richard G. Baraniuk, 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Model Corrected Low Rank Ptychography

Chinmay Hegde, Namrata Vaswani, Zhengyu Chen, 2018, 2018 25th IEEE International Conference on Image Processing (ICIP).

Alternating Phase Projected Gradient Descent with Generative Priors for Solving Compressive Phase Retrieval

Chinmay Hegde, M. Salman Asif, M. Salman Asif, 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Fast and Provable Algorithms for Learning Two-Layer Polynomial Neural Networks

Chinmay Hegde, Mohammadreza Soltani, C. Hegde, 2019, IEEE Transactions on Signal Processing.

Data-driven prognostics of lithium-ion rechargeable battery using bilinear kernel regression

Chinmay Hegde, Chao Hu, Charlie Hubbard, 2016 .

Provably Accurate Double-Sparse Coding

Chinmay Hegde, Raymond K. W. Wong, Thanh V. Nguyen, 2017, J. Mach. Learn. Res..

Fast and Near-Optimal Algorithms for Approximating Distributions by Histograms

Chinmay Hegde, Ilias Diakonikolas, Ludwig Schmidt, 2015, PODS.

A Nearly-Linear Time Framework for Graph-Structured Sparsity

Piotr Indyk, Chinmay Hegde, Ludwig Schmidt, 2015, ICML.

Nearly Linear-Time Model-Based Compressive Sensing

Piotr Indyk, Chinmay Hegde, Ludwig Schmidt, 2014, ICALP.

A Kaczmarz Algorithm for Solving Tree Based Distributed Systems of Equations pdf

Chinmay Hegde, Fritz Keinert, Eric S. Weber, 2019, Applied and Numerical Harmonic Analysis.

The Benefits of Side Information for Structured Phase Retrieval

Chinmay Hegde, M. Salman Asif, C. Hegde, 2021, 2020 28th European Signal Processing Conference (EUSIPCO).

Collaborative Deep Learning in Fixed Topology Networks

Chinmay Hegde, Soumik Sarkar, Zhanhong Jiang, 2017, NIPS.

SPIN: Iterative signal recovery on incoherent manifolds

Chinmay Hegde, Richard G. Baraniuk, Richard Baraniuk, 2012, 2012 IEEE International Symposium on Information Theory Proceedings.

Bilevel feature selection in nearly-linear time

Chinmay Hegde, C. Hegde, 2016, 2016 IEEE Statistical Signal Processing Workshop (SSP).

On Consensus-Optimality Trade-offs in Collaborative Deep Learning pdf

Chinmay Hegde, Soumik Sarkar, Zhanhong Jiang, 2018, Frontiers in Artificial Intelligence.

Nonlinear signal models: geometry, algorithms, and analysis

Chinmay Hegde, Rich Baraniuk, Richard Baraniuk, 2012 .

Multi-objective sensor-based replanning for a car-like robot

Lydia E. Kavraki, Mark Moll, Chinmay Hegde, 2012, 2012 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR).

On the Dynamics of Gradient Descent for Autoencoders

Chinmay Hegde, Raymond K. W. Wong, Thanh V. Nguyen, 2019, AISTATS.

Nano-Material Configuration Design with Deep Surrogate Langevin Dynamics

Chinmay Hegde, Youssef Mroueh, Giuseppe Romano, 2020, ICLR 2020.

Data-driven parallelizable traffic incident detection using spatio-temporally denoised robust thresholds

Chinmay Hegde, Pranamesh Chakraborty, Anuj Sharma, 2019, Transportation Research Part C: Emerging Technologies.

Algorithmic Aspects of Inverse Problems Using Generative Models

Chinmay Hegde, C. Hegde, 2018, 2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

Towards Provable Learning of Polynomial Neural Networks Using Low-Rank Matrix Estimation

Chinmay Hegde, Mohammadreza Soltani, C. Hegde, 2018, AISTATS.

Solving Linear Inverse Problems Using Gan Priors: An Algorithm with Provable Guarantees

Chinmay Hegde, Viraj Shah, C. Hegde, 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors

Chinmay Hegde, Gauri Jagatap, C. Hegde, 2019, NeurIPS.

Reconstruction from periodic nonlinearities, with applications to HDR imaging

Chinmay Hegde, Mohammadreza Soltani, Viraj Shah, 2017, 2017 51st Asilomar Conference on Signals, Systems, and Computers.

Sample-Efficient Algorithms for Recovering Structured Signals From Magnitude-Only Measurements

Chinmay Hegde, Gauri Jagatap, C. Hegde, 2017, IEEE Transactions on Information Theory.

A Forward-Backward Approach for Visualizing Information Flow in Deep Networks pdf

Chinmay Hegde, Soumik Sarkar, Thanh V. Nguyen, 2017, ArXiv.

A Provable Approach for Double-Sparse Coding

Chinmay Hegde, Raymond K. W. Wong, Thanh V. Nguyen, 2017, AAAI.

Recovery of compressible signals in unions of subspaces

Volkan Cevher, Chinmay Hegde, Richard G. Baraniuk, 2009, 2009 43rd Annual Conference on Information Sciences and Systems.

Attribute-Controlled Traffic Data Augmentation Using Conditional Generative Models

Chinmay Hegde, Soumik Sarkar, Ameya Joshi, 2019, CVPR Workshops.

Seismic feature extraction using steiner tree methods

Piotr Indyk, Chinmay Hegde, Ludwig Schmidt, 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Fast Low-Rank Matrix Estimation for Ill-Conditioned Matrices

Chinmay Hegde, Mohammadreza Soltani, 2018, 2018 IEEE International Symposium on Information Theory (ISIT).

Fast Algorithms for Demixing Sparse Signals From Nonlinear Observations

Chinmay Hegde, Mohammadreza Soltani, C. Hegde, 2016, IEEE Transactions on Signal Processing.

Sub-Diffraction Imaging Using Fourier Ptychography and Structured Sparsity

Chinmay Hegde, Namrata Vaswani, Zhengyu Chen, 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Signal Recovery on Incoherent Manifolds

Chinmay Hegde, Richard G. Baraniuk, Richard Baraniuk, 2012, IEEE Transactions on Information Theory.

Towards Sample-Optimal Methods for Solving Random Quadratic Equations with Structure

Chinmay Hegde, Gauri Jagatap, 2018, 2018 IEEE International Symposium on Information Theory (ISIT).

Compressive image acquisition and classification via secant projections

Chinmay Hegde, Aswin C. Sankaranarayanan, Richard G. Baraniuk, 2015 .

A Theoretical Analysis of Joint Manifolds pdf

Chinmay Hegde, Richard G. Baraniuk, Marco F. Duarte, 2009, ArXiv.

Fast recovery from a union of subspaces

Piotr Indyk, Chinmay Hegde, Ludwig Schmidt, 2016, NIPS.

Joint Manifolds for Data Fusion

Chinmay Hegde, Richard G. Baraniuk, Marco F. Duarte, 2010, IEEE Transactions on Image Processing.

Demixing sparse signals from nonlinear observations

Chinmay Hegde, Mohammadreza Soltani, C. Hegde, 2016, 2016 50th Asilomar Conference on Signals, Systems and Computers.

On Learning Sparsely Used Dictionaries from Incomplete Samples pdf

Chinmay Hegde, Akshay Soni, Thanh V. Nguyen, 2018, ICML.

PHASE RETRIEVAL FOR SIGNALS IN UNION OF SUBSPACES

Chinmay Hegde, M. Salman Asif, M. Salman Asif, 2018, 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

High Dimensional Data Fusion via Joint Manifold Learning

Chinmay Hegde, Richard G. Baraniuk, Marco F. Duarte, 2010, AAAI Fall Symposium: Manifold Learning and Its Applications.

Go with the flow: Optical flow-based transport operators for image manifolds

Chinmay Hegde, Aswin C. Sankaranarayanan, Richard G. Baraniuk, 2011, 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

Multi-robot target verification with reachability constraints

Lydia E. Kavraki, Mark Moll, Chinmay Hegde, 2012, 2012 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR).

Approximation Algorithms for Model-Based Compressive Sensing

Piotr Indyk, Chinmay Hegde, Ludwig Schmidt, 2014, IEEE Transactions on Information Theory.

Signal Reconstruction From Modulo Observations

Chinmay Hegde, Viraj Shah, C. Hegde, 2018, 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

Efficient Upsampling of Natural Images pdf

Chinmay Hegde, Fatih Murat Porikli, Oncel Tuzel, 2015, ArXiv.

Stable recovery of sparse vectors from random sinusoidal feature maps

Chinmay Hegde, Mohammadreza Soltani, 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Model-Based Compressive Sensing

Volkan Cevher, Chinmay Hegde, Richard G. Baraniuk, 2008, IEEE Transactions on Information Theory.

Approximation-Tolerant Model-Based Compressive Sensing

Piotr Indyk, Chinmay Hegde, Ludwig Schmidt, 2014, SODA.

Sparse Signal Recovery Using Markov Random Fields

Volkan Cevher, Chinmay Hegde, Richard G. Baraniuk, 2008, NIPS.

Reducing the Search Space for Hyperparameter Optimization Using Group Sparsity

Chinmay Hegde, Minsu Cho, C. Hegde, 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

A fast iterative algorithm for demixing sparse signals from nonlinear observations

Chinmay Hegde, Mohammadreza Soltani, C. Hegde, 2016, 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

Sampling and Recovery of Pulse Streams

Chinmay Hegde, Richard G. Baraniuk, Richard Baraniuk, 2010, IEEE Transactions on Signal Processing.

Fast inverse design of microstructures via generative invariance networks

Chinmay Hegde, Soumik Sarkar, Baskar Ganapathysubramanian, 2020, Nature Computational Science.

Texas Hold 'Em algorithms for distributed compressive sensing

Chinmay Hegde, Richard G. Baraniuk, Marco F. Duarte, 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

Learning ReLU Networks via Alternating Minimization pdf

Chinmay Hegde, Gauri Jagatap, C. Hegde, 2018, ArXiv.

Phase Retrieval Using Structured Sparsity: A Sample Efficient Algorithmic Framework pdf

Chinmay Hegde, Gauri Jagatap, C. Hegde, 2017, ArXiv.

A fast approximation algorithm for tree-sparse recovery

Piotr Indyk, Chinmay Hegde, Ludwig Schmidt, 2014, 2014 IEEE International Symposium on Information Theory.

Demixing structured superposition signals from periodic and aperiodic nonlinear observations

Chinmay Hegde, Mohammadreza Soltani, C. Hegde, 2017, 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

Low Rank Fourier Ptychography

Chinmay Hegde, Namrata Vaswani, Zhengyu Chen, 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Semantic Adversarial Attacks: Parametric Transformations That Fool Deep Classifiers

Chinmay Hegde, Soumik Sarkar, Ameya Joshi, 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

Fast Algorithms for Structured Sparsity

Piotr Indyk, Chinmay Hegde, Ludwig Schmidt, 2015, Bull. EATCS.

One-Shot Neural Architecture Search via Compressive Sensing pdf

Chinmay Hegde, Minsu Cho, Mohammadreza Soltani, 2019, ArXiv.

Physics-aware Deep Generative Models for Creating Synthetic Microstructures pdf

Baskar Ganapathysubramanian, Rahul Singh, Chinmay Hegde, 2018, ArXiv.

Near-isometric linear embeddings of manifolds

Chinmay Hegde, Aswin C. Sankaranarayanan, Richard G. Baraniuk, 2012, 2012 IEEE Statistical Signal Processing Workshop (SSP).

Encoding Invariances in Deep Generative Models pdf

Baskar Ganapathysubramanian, Chinmay Hegde, Soumik Sarkar, 2019, ArXiv.

Freeway Traffic Incident Detection from Cameras: A Semi-Supervised Learning Approach

Chinmay Hegde, Pranamesh Chakraborty, Anuj Sharma, 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

Parallel computing heuristics for low-rank matrix completion

Chinmay Hegde, Charlie Hubbard, 2017, 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

Fast, Sample-Efficient Algorithms for Structured Phase Retrieval

Chinmay Hegde, Gauri Jagatap, C. Hegde, 2017, NIPS.

Autoencoders Learn Generative Linear Models pdf

Chinmay Hegde, Raymond K. W. Wong, Thanh V. Nguyen, 2018, ArXiv.

Implicit Sparse Regularization: The Impact of Depth and Early Stopping pdf

Chinmay Hegde, Raymond K. W. Wong, Jiangyuan Li, 2021, NeurIPS.

Large-Scale Data-Driven Traffic Sensor Health Monitoring

Chinmay Hegde, Pranamesh Chakraborty, Tongge Huang, 2021, Journal of Big Data Analytics in Transportation.

Sparse signal recovery from modulo observations

Chinmay Hegde, Viraj Shah, 2021, EURASIP J. Adv. Signal Process..

Sphynx: ReLU-Efficient Network Design for Private Inference pdf

Chinmay Hegde, Zahra Ghodsi, Siddharth Garg, 2021, ArXiv.

Differentiable Spline Approximations pdf

Baskar Ganapathysubramanian, Adarsh Krishnamurthy, Chinmay Hegde, 2021, NeurIPS.

Cross-Gradient Aggregation for Decentralized Learning from Non-IID data

Yasaman Esfandiari, Chinmay Hegde, Soumik Sarkar, 2021, ICML.

The Stochastic Augmented Lagrangian method for domain adaptation

Chinmay Hegde, Dongxiang Jiang, Zhanhong Jiang, 2021, Knowl. Based Syst..

Provable Compressed Sensing With Generative Priors via Langevin Dynamics pdf

Chinmay Hegde, Gauri Jagatap, Thanh V. Nguyen, 2021, IEEE Transactions on Information Theory.

NeuFENet: Neural Finite Element Solutions with Theoretical Bounds for Parametric PDEs pdf

Baskar Ganapathysubramanian, Adarsh Krishnamurthy, Chinmay Hegde, 2021, Engineering with Computers.

NURBS-Diff: A Differentiable Programming Module for NURBS

Adarsh Krishnamurthy, Chinmay Hegde, Aditya Balu, 2021, Comput. Aided Des..

Adversarial Token Attacks on Vision Transformers pdf

Chinmay Hegde, Gauri Jagatap, Ameya Joshi, 2021, ArXiv.

MDPGT: Momentum-based Decentralized Policy Gradient Tracking pdf

Chinmay Hegde, Xian Yeow Lee, Zhanhong Jiang, 2021, ArXiv.

3D ANIMATION OF SINGLE STAGE BATCH DISTILLATION FOR DISTANCE LEARNING

Chinmay Hegde, David Broneske, Nicole Vorhauer, 2021, EDULEARN21 Proceedings.

Distributed Multigrid Neural Solvers on Megavoxel Domains

Baskar Ganapathysubramanian, Adarsh Krishnamurthy, Chinmay Hegde, 2021, SC21: International Conference for High Performance Computing, Networking, Storage and Analysis.

Binary 2D Morphologies of Polymer Phase Separation: Dataset and Python Toolbox

Baskar Ganapathysubramanian, Chinmay Hegde, Sambuddha Ghosal, 2019 .

MANIFOLD-BASED APPROACHES FOR IMPROVED CLASSIFICATION

Chinmay Hegde, Richard G. Baraniuk, Michael B. Wakin, 2007 .

Efficient Machine Learning Using Random Projections

Chinmay Hegde, Richard G. Baraniuk, Michael B. Wakin, 2007 .

Optical Flow-Based Transport on Image Manifolds

Chinmay Hegde, Aswin C. Sankaranarayanan, Richard G. Baraniuk, 2014 .

Fast Low-Rank Matrix Estimation without the Condition Number

Chinmay Hegde, Mohammadreza Soltani, C. Hegde, 2017 .

Fast Algorithms for Learning Latent Variables in Graphical Models

Chinmay Hegde, Mohammadreza Soltani, 2017 .