Search Results for author: Vikranth Dwaracherla

Found 5 papers, 3 papers with code

Evaluating Predictive Distributions: Does Bayesian Deep Learning Work?

1 code implementation9 Oct 2021 Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Botao Hao, Morteza Ibrahimi, Dieterich Lawson, Xiuyuan Lu, Brendan O'Donoghue, Benjamin Van Roy

This paper introduces \textit{The Neural Testbed}, which provides tools for the systematic evaluation of agents that generate such predictions.

Reinforcement Learning, Bit by Bit

no code implementations6 Mar 2021 Xiuyuan Lu, Benjamin Van Roy, Vikranth Dwaracherla, Morteza Ibrahimi, Ian Osband, Zheng Wen

Reinforcement learning agents have demonstrated remarkable achievements in simulated environments.

Langevin DQN

2 code implementations17 Feb 2020 Vikranth Dwaracherla, Benjamin Van Roy

Algorithms that tackle deep exploration -- an important challenge in reinforcement learning -- have relied on epistemic uncertainty representation through ensembles or other hypermodels, exploration bonuses, or visitation count distributions.

Motion-based Object Segmentation based on Dense RGB-D Scene Flow

1 code implementation14 Apr 2018 Lin Shao, Parth Shah, Vikranth Dwaracherla, Jeannette Bohg

Our model jointly estimates (i) the segmentation of the scene into an unknown but finite number of objects, (ii) the motion trajectories of these objects and (iii) the object scene flow.

Motion Segmentation Semantic Segmentation

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