| Backpropagating through the air: Deep learning at physical layer without channel models V Raj, S Kalyani IEEE Communications Letters 22 (11), 2278-2281, 2018 | 121 | 2018 |
| Spectrum access in cognitive radio using a two-stage reinforcement learning approach V Raj, I Dias, T Tholeti, S Kalyani IEEE Journal of Selected Topics in Signal Processing 12 (1), 20-34, 2018 | 101 | 2018 |
| Taming non-stationary bandits: A Bayesian approach V Raj, S Kalyani arXiv preprint arXiv:1707.09727, 2017 | 99 | 2017 |
| Design of communication systems using deep learning: A variational inference perspective V Raj, S Kalyani IEEE Transactions on Cognitive Communications and Networking 6 (4), 1320-1334, 2020 | 33 | 2020 |
| Beyond 5G: Leveraging cell free TDD massive MIMO using cascaded deep learning N Athreya, V Raj, S Kalyani IEEE Wireless Communications Letters 9 (9), 1533-1537, 2020 | 27 | 2020 |
| Deep reinforcement learning based blind mmWave MIMO beam alignment V Raj, N Nayak, S Kalyani IEEE Transactions on Wireless Communications 21 (10), 8772-8785, 2022 | 24 | 2022 |
| Measurement accuracy enhancement with multi-event detection using the deep learning approach in Raman distributed temperature sensors A Datta, V Raj, V Sankar, S Kalyani, B Srinivasan Optics express 29 (17), 26745-26764, 2021 | 17 | 2021 |
| HAPNEST: efficient, large-scale generation and evaluation of synthetic datasets for genotypes and phenotypes S Wharrie, Z Yang, V Raj, R Monti, R Gupta, Y Wang, A Martin, ... bioinformatics 39 (9), btad535, 2023 | 7 | 2023 |
| An aggregating strategy for shifting experts in discrete sequence prediction V Raj, S Kalyani arXiv preprint arXiv:1708.01744, 2017 | 5 | 2017 |
| Understanding learning dynamics of binary neural networks via information bottleneck V Raj, N Nayak, S Kalyani arXiv preprint arXiv:2006.07522, 2020 | 4 | 2020 |
| Leveraging online learning for CSS in frugal IoT network N Nayak, V Raj, S Kalyani IEEE Transactions on Cognitive Communications and Networking 6 (4), 1350-1364, 2020 | 3 | 2020 |
| Blind decoding in -Stable noise: An online learning approach V Raj, S Kalyani arXiv preprint arXiv:1906.09811, 2019 | 3 | 2019 |
| HAPNEST: an efficient tool for generating large-scale genetics datasets from limited training data S Wharrie, Z Yang, V Raj, R Monti, R Gupta, Y Wang, A Martin, ... NeurIPS 2022 Workshop on Synthetic Data for Empowering ML Research, 2022 | 2 | 2022 |
| A non-parametric multi-stage learning framework for cognitive spectrum access in iot networks T Tholeti, V Raj, S Kalyani CoRR, vol. abs/1804.11135, 2018 | 2 | 2018 |
| Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approach V Raj, T Cui, M Heinonen, P Marttinen International Conference on Artificial Intelligence and Statistics, 6741-6763, 2023 | 1 | 2023 |
| A comprehensive study on binary optimizer and its applicability N Nayak, V Raj, S Kalyani | 1 | 2020 |
| VMS: Interactive Visualization to Support the Sensemaking and Selection of Predictive Models C He, V Raj, H Moen, T Gröhn, C Wang, LM Peltonen, S Koivusalo, ... Proceedings of the 29th International Conference on Intelligent User …, 2024 | | 2024 |
| A fast method to generate hundreds of thousands of synthetic genomes and phenotypes S Wharrie, Z Yang, V Raj, R Gupta, R Monti, Y Wang, PF Palamara, ... European journal of human genetics 31 (Suppl. 1), 291-291, 2023 | | 2023 |
| Replication/NeurIPS 2019 Reproducibility Challenge N Nayak, V Raj, S Kalyani | | 2020 |
| A Centralized Multi-stage Non-parametric Learning Algorithm for Opportunistic Spectrum Access T Tholeti, V Raj, S Kalyani arXiv preprint arXiv:1804.11135, 2018 | | 2018 |
Sheetal KalyaniProfessor, Electrical Engineering, IIT Madras在 ee.iitm.ac.in 的电子邮件经过验证
