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Abhisek Kundu
Abhisek Kundu
Research Scientist, Intel Parallel Computing Labs, India
Verified email at intel.com
Title
Cited by
Cited by
Year
A study of BFLOAT16 for deep learning training
D Kalamkar, D Mudigere, N Mellempudi, D Das, K Banerjee, S Avancha, ...
arXiv preprint arXiv:1905.12322, 2019
1422019
Ternary neural networks with fine-grained quantization
N Mellempudi, A Kundu, D Mudigere, D Das, B Kaul, P Dubey
arXiv preprint arXiv:1705.01462, 2017
1052017
Mixed low-precision deep learning inference using dynamic fixed point
N Mellempudi, A Kundu, D Das, D Mudigere, B Kaul
arXiv preprint arXiv:1701.08978, 2017
252017
A note on randomized element-wise matrix sparsification
A Kundu, P Drineas
arXiv preprint arXiv:1404.0320, 2014
192014
Ternary residual networks
A Kundu, K Banerjee, N Mellempudi, D Mudigere, D Das, B Kaul, ...
arXiv preprint arXiv:1707.04679, 2017
112017
Multi-dimensional discovery of biomarker and phenotype complexes
PRO Payne, K Huang, A Kundu, J Zhang, TB Borlawsky
BMC bioinformatics 11 (9), 1-9, 2010
112010
A randomized rounding algorithm for sparse PCA
K Fountoulakis, A Kundu, EM Kontopoulou, P Drineas
ACM Transactions on Knowledge Discovery from Data (TKDD) 11 (3), 1-26, 2017
92017
Tensor processing primitives: a programming abstraction for efficiency and portability in deep learning workloads
E Georganas, D Kalamkar, S Avancha, M Adelman, C Anderson, A Breuer, ...
Proceedings of the International Conference for High Performance Computing …, 2021
82021
Recovering PCA and sparse PCA via hybrid- (l1, l2)sparse sampling of data elements
A Kundu, P Drineas, M Magdon-Ismail
The Journal of Machine Learning Research 18 (1), 2558-2591, 2017
72017
A study of bfloat16 for deep learning training (2019)
D Kalamkar, D Mudigere, N Mellempudi, D Das, K Banerjee, S Avancha, ...
arXiv preprint arXiv:1905.12322, 1905
61905
Ternary neural networks with fine-grained quantization. 2017
N Mellempudi, A Kundu, D Mudigere, D Das, B Kaul, P Dubey
arXiv preprint arxiv:1705.01462, 2017
52017
Relaxed leverage sampling for low-rank matrix completion
A Kundu
Information Processing Letters 124, 6-9, 2017
42017
Approximating sparse pca from incomplete data
A Kundu, P Drineas, M Magdon-Ismail
Advances in Neural Information Processing Systems 28, 2015
42015
Synthesis of individual handwriting in bangla script
BB Chaudhuri, A Kundu
Proceedings of the ICFHR, 2008
42008
K-TanH: hardware efficient activations for deep learning
A Kundu, S Srinivasan, EC Qin, D Kalamkar, NK Mellempudi, D Das, ...
arXiv preprint arXiv:1909.07729, 2019
32019
Incremental precision networks using residual inference and fine-grain quantization
A Kundu, N Mellempudi, D Mudigere, D Das
US Patent App. 15/869,515, 2018
32018
K-tanh: Efficient tanh for deep learning
A Kundu, A Heinecke, D Kalamkar, S Srinivasan, EC Qin, NK Mellempudi, ...
arXiv preprint arXiv:1909.07729, 2019
22019
Recovering PCA from Hybrid- Sparse Sampling of Data Elements
A Kundu, P Drineas, M Magdon-Ismail
arXiv preprint arXiv:1503.00547, 2015
12015
A randomized rounding algorithm for sparse PCA
P Drineas, K Fountoulakis, A Kundu
Computing Research Repository (CoRR), abs/1508.03337, 2015
12015
Identifying influential entries in a matrix
A Kundu, S Nambirajan, P Drineas
arXiv preprint arXiv:1310.3556, 2013
12013
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