no code implementations • 29 Jan 2020 • Stephen Mell, Olivia Brown, Justin Goodwin, Sung-Hyun Son
We present an approach for designing correct-by-construction neural networks (and other machine learning models) that are guaranteed to be consistent with a collection of input-output specifications before, during, and after algorithm training.
1 code implementation • 7 Jun 2019 • Taylor Killian, Justin Goodwin, Olivia Brown, Sung-Hyun Son
Capsule Networks attempt to represent patterns in images in a way that preserves hierarchical spatial relationships.
2 code implementations • 22 Mar 2019 • Andrew Silva, Taylor Killian, Ivan Dario Jimenez Rodriguez, Sung-Hyun Son, Matthew Gombolay
Decision trees are ubiquitous in machine learning for their ease of use and interpretability.
no code implementations • 26 Nov 2018 • Justin A. Goodwin, Olivia M. Brown, Taylor W. Killian, Sung-Hyun Son
Radio frequency (RF) sensors are used alongside other sensing modalities to provide rich representations of the world.
no code implementations • 11 May 2018 • Matthew Gombolay, Reed Jensen, Jessica Stigile, Toni Golen, Neel Shah, Sung-Hyun Son, Julie Shah
We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem.