Charles Edison Tripp

According to our database, Charles Edison Tripp authored at least 9 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations.

Charles Edison Tripp, Jordan Perr-Sauer, Jamil Gafur, Ambarish Nag, Avi Purkayastha, Sagi Zisman, Erik A. Bensen

CoRR, 2024

2022

A Modular and Transferable Reinforcement Learning Framework for the Fleet Rebalancing Problem.

Erotokritos Skordilis, Yi Hou, Charles Edison Tripp, Matthew Moniot, Peter A. Graf, David Biagioni

IEEE Trans. Intell. Transp. Syst., 2022

Multi-objective goal-directed optimization of de novo stable organic radicals for aqueous redox flow batteries.

Shree Sowndarya S. V., Jeffrey N. Law, Charles Edison Tripp, Dmitry Duplyakin, Erotokritos Skordilis, David Biagioni, Robert S. Paton, Peter C. St. John

Nat. Mach. Intell., 2022

graphenv: a Python library for reinforcement learning on graph search spaces.

David Biagioni, Charles Edison Tripp, Struan Clark, Dmitry Duplyakin, Jeffrey N. Law, Peter C. St. John

J. Open Source Softw., 2022

An Empirical Deep Dive into Deep Learning's Driving Dynamics.

Charles Edison Tripp, Jordan Perr-Sauer, Lucas Hayne, Monte Lunacek

CoRR, 2022

2021

A Cyber-Physical System for Freeway Ramp Meter Signal Control Using Deep Reinforcement Learning in a Connected Environment.

Yi Hou, Xiangyu Zhang, Peter A. Graf, Charles Edison Tripp, David Biagioni

Proceedings of the 24th IEEE International Intelligent Transportation Systems Conference, 2021

2018

FRC: a high-performance concurrent parallel deferred reference counter for C++.

Charles Tripp, David Hyde, Benjamin Grossman-Ponemon

Proceedings of the 2018 ACM SIGPLAN International Symposium on Memory Management, 2018

2013

Approximate Kalman Filter Q-Learning for Continuous State-Space MDPs.

Charles Tripp, Ross D. Shachter

Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

2012

Backtracking for More Efficient Large Scale Dynamic Programming.

Charles Tripp, Ross D. Shachter

Proceedings of the 11th International Conference on Machine Learning and Applications, 2012