no code implementations • 1 Dec 2023 • Viraj Mehta, Vikramjeet Das, Ojash Neopane, Yijia Dai, Ilija Bogunovic, Jeff Schneider, Willie Neiswanger
Preference-based feedback is important for many applications in reinforcement learning where direct evaluation of a reward function is not feasible.
no code implementations • 21 Jul 2023 • Viraj Mehta, Ojash Neopane, Vikramjeet Das, Sen Lin, Jeff Schneider, Willie Neiswanger
Preference-based feedback is important for many applications where direct evaluation of a reward function is not feasible.
no code implementations • 8 Dec 2021 • Ojash Neopane, Aaditya Ramdas, Aarti Singh
We consider a variant of the best arm identification (BAI) problem in multi-armed bandits (MAB) in which there are two sets of arms (source and target), and the objective is to determine the best target arm while only pulling source arms.
1 code implementation • 7 May 2017 • Xin-Yu Zhang, Srinjoy Das, Ojash Neopane, Ken Kreutz-Delgado
In support of such applications, various FPGA accelerator architectures have been proposed for convolutional neural networks (CNNs) that enable high performance for classification tasks at lower power than CPU and GPU processors.
no code implementations • 18 Feb 2016 • Ojash Neopane, Srinjoy Das, Ery Arias-Castro, Kenneth Kreutz-Delgado
Restricted Boltzmann Machines and Deep Belief Networks have been successfully used in probabilistic generative model applications such as image occlusion removal, pattern completion and motion synthesis.