no code implementations • 3 Aug 2020 • Ivan Papusha, Rosa Wu, Joshua Brulé, Yanni Kouskoulas, Daniel Genin, Aurora Schmidt
There is great interest in using formal methods to guarantee the reliability of deep neural networks.
1 code implementation • 21 Dec 2018 • Joshua Brulé
This paper introduces Whittemore, a language for causal programming.
no code implementations • 4 May 2018 • Joshua Brulé
This paper proposes a causal inference relation and causal programming as general frameworks for causal inference with structural causal models.
no code implementations • 5 Aug 2017 • Joshua Brulé
This paper introduces a causation coefficient which is defined in terms of probabilistic causal models.
no code implementations • 19 Mar 2016 • Joshua Brulé
This paper considers the computational power of constant size, dynamic Bayesian networks.
no code implementations • 19 Mar 2016 • Joshua Brulé, Kevin Engel, Nick Fung, Isaac Julien
We apply genetic programming techniques to the `shepherding' problem, in which a group of one type of animal (sheep dogs) attempts to control the movements of a second group of animals (sheep) obeying flocking behavior.
1 code implementation • 18 Jul 2015 • Fan Du, Joshua Brulé, Peter Enns, Varun Manjunatha, Yoav Segev
Understanding the quality and usage of public transportation resources is important for schedule optimization and resource allocation.
Human-Computer Interaction