16 citations · 53 across the 8 of their papers we have counts for
8 papers
A Unified Theory of Compositionality, Modularity, and Interpretability in Markov Decision Processes
Thomas J. Ringstrom, Paul R. Schrater
We introduce Option Kernel Bellman Equations (OKBEs) for a new reward-free Markov Decision Process. Rather than a value function, OKBEs directly construct and optimize a predictive…
A Unified Paths Perspective for Pruning at Initialization
Thomas Gebhart, Udit Saxena, Paul Schrater
A number of recent approaches have been proposed for pruning neural network parameters at initialization with the goal of reducing the size and computational burden of models while…
The structure of behavioral data
Aurélien Defossez, Morteza Ansarinia, Brice Clocher +3
For more than a century, scientists have been collecting behavioral data--an increasing fraction of which is now being publicly shared so other researchers can reuse them to replic…
Neuromatch Academy: Teaching Computational Neuroscience with global accessibility
Tara van Viegen, Athena Akrami, Kate Bonnen +32
Neuromatch Academy designed and ran a fully online 3-week Computational Neuroscience summer school for 1757 students with 191 teaching assistants working in virtual inverted (or fl…
Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics
Minhae Kwon, Saurabh Daptardar, Paul Schrater +1
A fundamental question in neuroscience is how the brain creates an internal model of the world to guide actions using sequences of ambiguous sensory information. This is naturally…
Appreciating the variety of goals in computational neuroscience
Konrad P. Kording, Gunnar Blohm, Paul Schrater +1
Within computational neuroscience, informal interactions with modelers often reveal wildly divergent goals. In this opinion piece, we explicitly address the diversity of goals that…
Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics
Saurabh Daptardar, Paul Schrater, Xaq Pitkow
Continuous control and planning remains a major challenge in robotics and machine learning. Neuroscience offers the possibility of learning from animal brains that implement highly…
Constraint Satisfaction Propagation: Non-stationary Policy Synthesis for Temporal Logic Planning
Thomas J. Ringstrom, Paul R. Schrater
Problems arise when using reward functions to capture dependencies between sequential time-constrained goal states because the state-space must be prohibitively expanded to accommo…