10 citations · 10 across the 2 of their papers we have counts for
4 papers
Evaluating High-Order Predictive Distributions in Deep Learning
Ian Osband, Zheng Wen, Seyed Mohammad Asghari +3
Most work on supervised learning research has focused on marginal predictions. In decision problems, joint predictive distributions are essential for good performance. Previous wor…
Hypermodels for Exploration
Vikranth Dwaracherla, Xiuyuan Lu, Morteza Ibrahimi +3
We study the use of hypermodels to represent epistemic uncertainty and guide exploration. This generalizes and extends the use of ensembles to approximate Thompson sampling. The co…
Parameterized Indexed Value Function for Efficient Exploration in Reinforcement Learning
Tian Tan, Zhihan Xiong, Vikranth R. Dwaracherla
It is well known that quantifying uncertainty in the action-value estimates is crucial for efficient exploration in reinforcement learning. Ensemble sampling offers a relatively co…
Motion-based Object Segmentation based on Dense RGB-D Scene Flow
Lin Shao, Parth Shah, Vikranth Dwaracherla +1
Given two consecutive RGB-D images, we propose a model that estimates a dense 3D motion field, also known as scene flow. We take advantage of the fact that in robot manipulation sc…