43 citations · 45 across the 3 of their papers we have counts for
3 papers
cs.NE2022
Neuro-Nav: A Library for Neurally-Plausible Reinforcement Learning
Arthur Juliani, Samuel Barnett, Brandon Davis +2
In this work we propose Neuro-Nav, an open-source library for neurally plausible reinforcement learning (RL). RL is among the most common modeling frameworks for studying decision…
cs.CL2021★ 2 cited
A pragmatic account of the weak evidence effect
Samuel A. Barnett, Thomas L. Griffiths, Robert D. Hawkins
Language is not only used to transmit neutral information; we often seek to persuade by arguing in favor of a particular view. Persuasion raises a number of challenges for classica…
cs.LG2018★ 43 cited
Convergence Problems with Generative Adversarial Networks (GANs)
Samuel A. Barnett
Generative adversarial networks (GANs) are a novel approach to generative modelling, a task whose goal it is to learn a distribution of real data points. They have often proved dif…