| 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 | 336 | 2019 |
| 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 | 136 | 2017 |
| 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 | 29 | 2017 |
| A note on randomized element-wise matrix sparsification A Kundu, P Drineas arXiv preprint arXiv:1404.0320, 2014 | 24 | 2014 |
| 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 | 22 | 2021 |
| Incremental precision networks using residual inference and fine-grain quantization A Kundu, N Mellempudi, D Mudigere, D Das US Patent 11,556,772, 2023 | 21 | 2023 |
| Recovering PCA and sparse PCA via hybrid-(l1, l2) sparse sampling of data elements A Kundu, P Drineas, M Magdon-Ismail Journal of Machine Learning Research 18 (75), 1-34, 2017 | 20 | 2017 |
| Ternary residual networks A Kundu, K Banerjee, N Mellempudi, D Mudigere, D Das, B Kaul, ... arXiv preprint arXiv:1707.04679, 2017 | 15 | 2017 |
| 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 | 15 | 1905 |
| Multi-dimensional discovery of biomarker and phenotype complexes PRO Payne, K Huang, K Keen-Circle, A Kundu, J Zhang, TB Borlawsky BMC bioinformatics 11, 1-9, 2010 | 11 | 2010 |
| 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 | 9 | 2017 |
| Approximating sparse pca from incomplete data A Kundu, P Drineas, M Magdon-Ismail Advances in Neural Information Processing Systems 28, 2015 | 7 | 2015 |
| A study of BFLOAT16 for deep learning training. arXiv 2019 D Kalamkar, D Mudigere, N Mellempudi, D Das, K Banerjee, S Avancha, ... arXiv preprint arXiv:1905.12322, 2019 | 6 | 2019 |
| A study of BFLOAT16 for deep learning training. arXiv D Kalamkar, D Mudigere, N Mellempudi, D Das, K Banerjee, S Avancha, ... arXiv preprint arXiv:1905.12322, 2019 | 6 | 2019 |
| Synthesis of individual handwriting in bangla script BB Chaudhuri, A Kundu Proceedings of the ICFHR, 2008 | 6 | 2008 |
| 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 | 5 | 2017 |
| Measuring frequency and period separations in red-giant stars using machine learning S Dhanpal, O Benomar, S Hanasoge, A Kundu, D Dhuri, D Das, B Kaul The Astrophysical Journal 928 (2), 188, 2022 | 4 | 2022 |
| 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 | 4 | 2019 |
| AUTOSPARSE: Towards Automated Sparse Training of Deep Neural Networks A Kundu, NK Mellempudi, DT Vooturi, B Kaul, P Dubey Workshop at International Conference on Learning Representation (ICLR 2023), 2023 | 3 | 2023 |
| Instructions for fused multiply-add operations with variable precision input operands D Das, NK Mellempudi, M Dutta, A Kumar, D Mudigere, A Kundu US Patent 10,528,346, 2020 | 3 | 2020 |
