Model-based compressive sensing RG Baraniuk, V Cevher, MF Duarte, C Hegde IEEE Transactions on information theory 56 (4), 1982-2001, 2010 | 2081 | 2010 |
Sparse signal recovery using markov random fields V Cevher, C Hegde, MF Duarte, RG Baraniuk Neural Information Processing Systems, 2009 | 222 | 2009 |
Collaborative deep learning in fixed topology networks Z Jiang, A Balu, C Hegde, S Sarkar Neural Information Processing Systems, 2017 | 197 | 2017 |
Solving linear inverse problems using gan priors: An algorithm with provable guarantees V Shah, C Hegde 2018 IEEE international conference on acoustics, speech and signal …, 2018 | 186 | 2018 |
Random projections for manifold learning C Hegde, MB Wakin, RG Baraniuk Neural Information Processing Systems, 2007 | 175 | 2007 |
An introduction to compressive sensing R Baraniuk, MA Davenport, MF Duarte, C Hegde Connexions e-textbook, 24-76, 2011 | 157 | 2011 |
Semantic adversarial attacks: Parametric transformations that fool deep classifiers A Joshi, A Mukherjee, S Sarkar, C Hegde Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 113 | 2019 |
Recovery of clustered sparse signals from compressive measurements V Cevher, P Indyk, C Hegde, RG Baraniuk SampTA, 2009 | 113 | 2009 |
A nearly-linear time framework for graph-structured sparsity C Hegde, P Indyk, L Schmidt International Conference on Machine Learning, 928-937, 2015 | 111 | 2015 |
NuMax: a convex approach for learning near-isometric linear embeddings C Hegde, AC Sankaranarayanan, W Yin, RG Baraniuk Signal Processing, IEEE Transactions on 63 (22), 6109-6121, 2015 | 107 | 2015 |
Joint manifolds for data fusion MA Davenport, C Hegde, MF Duarte, RG Baraniuk Image Processing, IEEE Transactions on 19 (10), 2580-2594, 2010 | 79 | 2010 |
Algorithmic guarantees for inverse imaging with untrained network priors G Jagatap, C Hegde Neural Information Processing Systems, 2019 | 78 | 2019 |
Approximation algorithms for model-based compressive sensing C Hegde, P Indyk, L Schmidt IEEE Transactions on Information Theory 61 (9), 5129-5147, 2015 | 75 | 2015 |
Compressive sensing recovery of spike trains using a structured sparsity model C Hegde, MF Duarte, V Cevher SPARS'09-Signal Processing with Adaptive Sparse Structured Representations, 2009 | 65 | 2009 |
Sampling and recovery of pulse streams C Hegde, RG Baraniuk Signal Processing, IEEE Transactions on 59 (4), 1505-1517, 2011 | 64 | 2011 |
Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents XY Lee, S Ghadai, KL Tan, C Hegde, S Sarkar AAAI, 2020 | 63 | 2020 |
Fast and near-optimal algorithms for approximating distributions by histograms J Acharya, I Diakonikolas, C Hegde, JZ Li, L Schmidt Proceedings of the 34th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of …, 2015 | 61 | 2015 |
On the computational complexity of self-attention FD Keles, PM Wijewardena, C Hegde International Conference on Algorithmic Learning Theory, 597-619, 2023 | 60 | 2023 |
Fast, sample-efficient algorithms for structured phase retrieval G Jagatap, C Hegde Advances in Neural Information Processing Systems, 4917-4927, 2017 | 56 | 2017 |
Alternating phase projected gradient descent with generative priors for solving compressive phase retrieval R Hyder, V Shah, C Hegde, MS Asif ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 54 | 2019 |