12 citations · 22 across the 6 of their papers we have counts for
6 papers
EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning
Siddharth Aravindan, Dixant Mittal, Wee Sun Lee
Posterior Sampling for Reinforcement Learning (PSRL) is a well-known algorithm that augments model-based reinforcement learning (MBRL) algorithms with Thompson sampling. PSRL maint…
Instructive3D: Editing Large Reconstruction Models with Text Instructions
Kunal Kathare, Ankit Dhiman, K Vikas Gowda +4
Transformer based methods have enabled users to create, modify, and comprehend text and image data. Recently proposed Large Reconstruction Models (LRMs) further extend this by prov…
ExPoSe: Combining State-Based Exploration with Gradient-Based Online Search
Dixant Mittal, Siddharth Aravindan, Wee Sun Lee
Online tree-based search algorithms iteratively simulate trajectories and update action-values for a set of states stored in a tree structure. It works reasonably well in practice…
State-Aware Variational Thompson Sampling for Deep Q-Networks
Siddharth Aravindan, Wee Sun Lee
Thompson sampling is a well-known approach for balancing exploration and exploitation in reinforcement learning. It requires the posterior distribution of value-action functions to…
An Analysis of Frame-skipping in Reinforcement Learning
Shivaram Kalyanakrishnan, Siddharth Aravindan, Vishwajeet Bagdawat +5
In the practice of sequential decision making, agents are often designed to sense state at regular intervals of time steps, , ignoring state information in between sensi…
Learning to Prune Deep Neural Networks via Reinforcement Learning
Manas Gupta, Siddharth Aravindan, Aleksandra Kalisz +2
This paper proposes PuRL - a deep reinforcement learning (RL) based algorithm for pruning neural networks. Unlike current RL based model compression approaches where feedback is gi…