Chinmay Hegde
Chinmay Hegde
Assistant Professor, Iowa State University / New York University
Verifierad e-postadress på iastate.edu - Startsida
TitelCiteras avÅr
Model-based compressive sensing
RG Baraniuk, V Cevher, MF Duarte, C Hegde
IEEE Transactions on information theory 56 (4), 1982-2001, 2010
16502010
Sparse signal recovery using markov random fields
V Cevher, MF Duarte, C Hegde, R Baraniuk
Advances in Neural Information Processing Systems, 257-264, 2009
1962009
Random projections for manifold learning
C Hegde, MB Wakin, RG Baraniuk
Neural Information Processing Systems, 2007
1402007
Recovery of clustered sparse signals from compressive measurements
V Cevher, P Indyk, C Hegde, RG Baraniuk
RICE UNIV HOUSTON TX DEPT OF ELECTRICAL AND COMPUTER ENGINEERING, 2009
1012009
An introduction to compressive sensing
R Baraniuk, MA Davenport, MF Duarte, C Hegde
Connexions e-textbook, 24-76, 2011
1002011
A nearly-linear time framework for graph-structured sparsity
C Hegde, P Indyk, L Schmidt
International Conference on Machine Learning, 928-937, 2015
652015
Joint manifolds for data fusion
MA Davenport, C Hegde, MF Duarte, RG Baraniuk
Image Processing, IEEE Transactions on 19 (10), 2580-2594, 2010
602010
Compressive sensing recovery of spike trains using a structured sparsity model
C Hegde, MF Duarte, V Cevher
602009
Sampling and recovery of pulse streams
C Hegde, RG Baraniuk
Signal Processing, IEEE Transactions on 59 (4), 1505-1517, 2011
572011
Approximation algorithms for model-based compressive sensing
C Hegde, P Indyk, L Schmidt
IEEE Transactions on Information Theory 61 (9), 5129-5147, 2015
542015
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
492015
NuMax: A convex approach for learning near-isometric linear embeddings
C Hegde, AC Sankaranarayanan, W Yin, RG Baraniuk
IEEE Transactions on Signal Processing 63 (22), 6109-6121, 2015
492015
Collaborative deep learning in fixed topology networks
Z Jiang, A Balu, C Hegde, S Sarkar
Advances in Neural Information Processing Systems, 5904-5914, 2017
402017
Approximation-tolerant model-based compressive sensing
C Hegde, P Indyk, L Schmidt
Proceedings of the twenty-fifth annual ACM-SIAM symposium on Discrete …, 2014
372014
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
352018
Signal recovery on incoherent manifolds
C Hegde, RG Baraniuk
Information Theory, IEEE Transactions on 58 (12), 7204-7214, 2012
332012
A fast approximation algorithm for tree-sparse recovery
C Hegde, P Indyk, L Schmidt
2014 IEEE International Symposium on Information Theory, 1842-1846, 2014
322014
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
262015
Recovery of compressible signals in unions of subspaces
MF Duarte, C Hegde, V Cevher, RG Baraniuk
Information Sciences and Systems, 2009. CISS 2009. 43rd Annual Conference on …, 2009
252009
The constrained earth mover distance model, with applications to compressive sensing
L Schmidt, C Hegde, P Indyk
10th Intl. Conf. on Sampling Theory and Appl.(SAMPTA), 2013
242013
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Artiklar 1–20