On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning A Dutta, EH Bergou, AM Abdelmoniem, CY Ho, AN Sahu, M Canini, ... AAAI-20 - Thirty-fourth AAAI Conference on Artificial Intelligence 34 (4 …, 2020 | 95 | 2020 |
GRACE: A compressed communication framework for distributed machine learning H Xu, CY Ho, AM Abdelmoniem, A Dutta, EH Bergou, K Karatsenidis, ... 2021 IEEE 41st International Conference on Distributed Computing Systems …, 2021 | 89 | 2021 |
Compressed communication for distributed deep learning: Survey and quantitative evaluation H Xu, CY Ho, AM Abdelmoniem, A Dutta, EH Bergou, K Karatsenidis, ... | 80 | 2020 |
Rethinking gradient sparsification as total error minimization A Sahu, A Dutta, A M Abdelmoniem, T Banerjee, M Canini, P Kalnis Advances in Neural Information Processing Systems 34, 8133-8146, 2021 | 52 | 2021 |
Deepreduce: A sparse-tensor communication framework for federated deep learning H Xu, K Kostopoulou, A Dutta, X Li, A Ntoulas, P Kalnis Advances in Neural Information Processing Systems 34, 21150-21163, 2021 | 36 | 2021 |
Huffman Coding Based Encoding Techniques for Fast Distributed Deep Learning RR Gajjala, S Banchhor, AM Abdelmoniem, A Dutta, M Canini, P Kalnis Proceedings of ACM CoNEXT'20: 1st Workshop on Distributed Machine Learning …, 2020 | 31 | 2020 |
Fast Detection of Compressively-Sensed IR Targets Using Stochastically Trained Least Squares and Compressed Quadratic Correlation Filters B Millikan, A Dutta, Q Sun, H Foroosh IEEE Transactions on Aerospace and Electronic Systems 53 (5), 2449-2461, 2017 | 22* | 2017 |
A Nonconvex Projection Method for Robust PCA A Dutta, F Hanzely, P Richtárik AAAI-19 - Thirty-Third AAAI Conference on Artificial Intelligence, 2019 | 20 | 2019 |
On a Problem of Weighted Low Rank Approximation of Matrices A Dutta, X Li SIAM Journal on Matrix Analysis and Applications 38 (2), 530-553, 2017 | 20 | 2017 |
Initialized Iterative Reweighted Least Squares for Automatic Target Recognition B Millikan, A Dutta, N Rahnavard, Q Sun, H Foroosh MILCOM 2015 - In proceedings of IEEE Military Communications Conference 2015 …, 2015 | 18 | 2015 |
Weighted low rank approximation for background estimation problems A Dutta, X Li ICCVW-2017-IEEE International Conference on Computer Vision Workshops (ICCVW), 2017 | 14 | 2017 |
Weighted low-rank approximation of matrices and background modeling A Dutta, X Li, P Richtárik arXiv preprint arXiv:1804.06252, 2018 | 13 | 2018 |
Weighted Singular Value Thresholding and its Applications to Background Estimation A Dutta, B Gong, X Li, M Shah | 13 | 2017 |
Shrinkage Function And Its Applications In Matrix Approximation T Boas, A Dutta, X Li, K Mercier, E Niderman Electronic Journal of Linear Algebra 32, 163–171, 2017 | 12 | 2017 |
DeepReduce: A sparse-tensor communication framework for distributed deep learning K Kostopoulou, H Xu, A Dutta, X Li, A Ntoulas, P Kalnis arXiv preprint arXiv:2102.03112, 2021 | 11 | 2021 |
A Batch-Incremental Video Background Estimation Model using Weighted Low-Rank Approximation of Matrices A Dutta, X Li, P Richtarik ICCVW 2017- IEEE International Conference on Computer Vision Workshops (ICCVW), 2017 | 11 | 2017 |
Weighted Low-Rank Approximation of Matrices: Some Analytical and Numerical Aspects A Dutta Ph.D. Dissertation, Department of Mathematics, University of Central Florida., 2017 | 11 | 2017 |
A Fast Algorithm for a Weighted Low-Rank Approximation A Dutta, X Li International Association for Pattern Recognition (IAPR) conference on …, 2017 | 10 | 2017 |
Personalized Federated Learning with Communication Compression E Bergou, KP Burlachenko, A Dutta, P Richtárik Transactions on Machine Learning Research, 2023 | 8* | 2023 |
Online and Batch Supervised Background Estimation via L1 Regression A Dutta, P Richtarik WACV 2019 – IEEE Winter Conference on the Applications of Computer Vision …, 2019 | 8 | 2019 |