most citedLearning model-based strategies in simple environments with hierarchical q-networks

1 citations · 1 across the 1 of their papers we have counts for

collaborators

6 papers

q-bio.NC2021

A critical reappraisal of predicting suicidal ideation using fMRI

Timothy Verstynen, Konrad Kording

For many psychiatric disorders, neuroimaging offers a potential for revolutionizing diagnosis, and potentially treatment, by providing access to preverbal mental processes. In thei…

cs.LG2018

Better Safe than Sorry: Evidence Accumulation Allows for Safe Reinforcement Learning

Akshat Agarwal, Abhinau Kumar, Kyle Dunovan +3

In the real world, agents often have to operate in situations with incomplete information, limited sensing capabilities, and inherently stochastic environments, making individual o…

cs.AI2018

Combining imagination and heuristics to learn strategies that generalize

Erik J Peterson, Necati Alp Müyesser, Timothy Verstynen +1

Deep reinforcement learning can match or exceed human performance in stable contexts, but with minor changes to the environment artificial networks, unlike humans, often cannot ada…

q-bio.NC2018

Cognitive chimera states in human brain networks

Kanika Bansal, Javier O. Garcia, Steven H. Tompson +3

The human brain is a complex dynamical system that gives rise to cognition through spatiotemporal patterns of coherent and incoherent activity between brain regions. As different r…

stat.AP2018

Local White Matter Architecture Defines Functional Brain Dynamics

Yo Joong Choe, Sivaraman Balakrishnan, Aarti Singh +2

Large bundles of myelinated axons, called white matter, anatomically connect disparate brain regions together and compose the structural core of the human connectome. We recently p…

cs.AI20181 cited

Learning model-based strategies in simple environments with hierarchical q-networks

Necati Alp Muyesser, Kyle Dunovan, Timothy Verstynen

Recent advances in deep learning have allowed artificial agents to rival human-level performance on a wide range of complex tasks; however, the ability of these networks to learn g…