Search Results for author: Vikranth Dwaracherla

Found 13 papers, 6 papers with code

Epistemic Neural Networks

1 code implementation NeurIPS 2023 Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, Benjamin Van Roy

We introduce the epinet: an architecture that can supplement any conventional neural network, including large pretrained models, and can be trained with modest incremental computation to estimate uncertainty.

Approximate Thompson Sampling via Epistemic Neural Networks

1 code implementation18 Feb 2023 Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, Benjamin Van Roy

Further, we demonstrate that the \textit{epinet} -- a small additive network that estimates uncertainty -- matches the performance of large ensembles at orders of magnitude lower computational cost.

Thompson Sampling

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 Object +3

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.

Computational Efficiency Open-Ended Question Answering +2

Reinforcement Learning, Bit by Bit

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

To illustrate concepts, we design simple agents that build on them and present computational results that highlight data efficiency.

reinforcement-learning Reinforcement Learning (RL)

Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping

no code implementations8 Jun 2022 Vikranth Dwaracherla, Zheng Wen, Ian Osband, Xiuyuan Lu, Seyed Mohammad Asghari, Benjamin Van Roy

In machine learning, an agent needs to estimate uncertainty to efficiently explore and adapt and to make effective decisions.

Robustness of Epinets against Distributional Shifts

no code implementations1 Jul 2022 Xiuyuan Lu, Ian Osband, Seyed Mohammad Asghari, Sven Gowal, Vikranth Dwaracherla, Zheng Wen, Benjamin Van Roy

However, these improvements are relatively small compared to the outstanding issues in distributionally-robust deep learning.

Efficient Exploration for LLMs

no code implementations1 Feb 2024 Vikranth Dwaracherla, Seyed Mohammad Asghari, Botao Hao, Benjamin Van Roy

We present evidence of substantial benefit from efficient exploration in gathering human feedback to improve large language models.

Efficient Exploration Thompson Sampling

Cannot find the paper you are looking for? You can Submit a new open access paper.