no code implementations • 9 Jul 2020 • Manas Gupta, Siddharth Aravindan, Aleksandra Kalisz, Vijay Chandrasekhar, Lin Jie
PuRL achieves more than 80% sparsity on the ResNet-50 model while retaining a Top-1 accuracy of 75. 37% on the ImageNet dataset.
no code implementations • 7 Feb 2021 • Siddharth Aravindan, Wee Sun Lee
We derive a variational Thompson sampling approximation for DQNs which uses a deep network whose parameters are perturbed by a learned variational noise distribution.
no code implementations • 7 Feb 2021 • Shivaram Kalyanakrishnan, Siddharth Aravindan, Vishwajeet Bagdawat, Varun Bhatt, Harshith Goka, Archit Gupta, Kalpesh Krishna, Vihari Piratla
In this paper, we investigate the role of the parameter $d$ in RL; $d$ is called the "frame-skip" parameter, since states in the Atari domain are images.
no code implementations • 29 Sep 2021 • Siddharth Aravindan, Dixant Mittal, Wee Sun Lee
These layers rely on Gaussian dropouts and are inserted in between the layers of the deep neural network model to help facilitate variational Thompson sampling.
1 code implementation • 3 Feb 2022 • Dixant Mittal, Siddharth Aravindan, Wee Sun Lee
Depending upon the smoothness of the action-value function, one approach to overcoming this issue is through online learning, where information is interpolated among similar states; Policy Gradient Search provides a practical algorithm to achieve this.