19 citations · 52 across the 12 of their papers we have counts for
14 papers
Automatic Gradient Descent: Deep Learning without Hyperparameters
Jeremy Bernstein, Chris Mingard, Kevin Huang +2
The architecture of a deep neural network is defined explicitly in terms of the number of layers, the width of each layer and the general network topology. Existing optimisation fr…
End-to-End Imitation Learning with Safety Guarantees using Control Barrier Functions
Ryan K. Cosner, Yisong Yue, Aaron D. Ames
Imitation learning (IL) is a learning paradigm which can be used to synthesize controllers for complex systems that mimic behavior demonstrated by an expert (user or control algori…
Neurosymbolic Programming for Science
Jennifer J. Sun, Megan Tjandrasuwita, Atharva Sehgal +4
Neurosymbolic Programming (NP) techniques have the potential to accelerate scientific discovery. These models combine neural and symbolic components to learn complex patterns and r…
Neural Gaits: Learning Bipedal Locomotion via Control Barrier Functions and Zero Dynamics Policies
Ivan Dario Jimenez Rodriguez, Noel Csomay-Shanklin, Yisong Yue +1
This work presents Neural Gaits, a method for learning dynamic walking gaits through the enforcement of set invariance that can be refined episodically using experimental data from…
MLNav: Learning to Safely Navigate on Martian Terrains
Shreyansh Daftry, Neil Abcouwer, Tyler Del Sesto +7
We present MLNav, a learning-enhanced path planning framework for safety-critical and resource-limited systems operating in complex environments, such as rovers navigating on Mars.…
Self-Supervised Online Learning for Safety-Critical Control using Stereo Vision
Ryan K. Cosner, Ivan D. Jimenez Rodriguez, Tamas G. Molnar +4
With the increasing prevalence of complex vision-based sensing methods for use in obstacle identification and state estimation, characterizing environment-dependent measurement err…