4 citations · 6 across the 3 of their papers we have counts for
6 papers
JORLDY: a fully customizable open source framework for reinforcement learning
Kyushik Min, Hyunho Lee, Kwansu Shin +4
Recently, Reinforcement Learning (RL) has been actively researched in both academic and industrial fields. However, there exist only a few RL frameworks which are developed for res…
Safe Predictors for Enforcing Input-Output Specifications
Stephen Mell, Olivia Brown, Justin Goodwin +1
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-out…
Kernelized Capsule Networks
Taylor Killian, Justin Goodwin, Olivia Brown +1
Capsule Networks attempt to represent patterns in images in a way that preserves hierarchical spatial relationships. Additionally, research has demonstrated that these techniques m…
Optimization Methods for Interpretable Differentiable Decision Trees in Reinforcement Learning
Andrew Silva, Taylor Killian, Ivan Dario Jimenez Rodriguez +2
Decision trees are ubiquitous in machine learning for their ease of use and interpretability. Yet, these models are not typically employed in reinforcement learning as they cannot…
Learning Robust Representations for Automatic Target Recognition
Justin A. Goodwin, Olivia M. Brown, Taylor W. Killian +1
Radio frequency (RF) sensors are used alongside other sensing modalities to provide rich representations of the world. Given the high variability of complex-valued target responses…
Human-Machine Collaborative Optimization via Apprenticeship Scheduling
Matthew Gombolay, Reed Jensen, Jessica Stigile +4
Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficul…