neural networks research group
areas • people • projects • demos • publications • software/data •
Garrett Bingham
Ph.D. Student
Garrett is a fourth-year Ph.D. student. His research utilizes evolutionary algorithms and meta-learning to optimize deep learning workflows. Prior to UT Austin, Garrett received a B.S. in Computer Science and Mathematics from Yale University.
Homepage:
https://garrettbingham.com
Publications
AutoInit: Analytic Signal-Preserving Weight Initialization for Neural Networks Garrett Bingham and Risto Miikkulainen In Proceedings of the 37th AAAI Conference on Artificial Intelligence, 2023. (also arXiv:20... 2023
Efficient Activation Function Optimization through Surrogate Modeling Garrett Bingham and Risto Miikkulainen arXiv:2301.05785, 2023. 2023
Discovering Parametric Activation Functions Garrett Bingham and Risto Miikkulainen Neural Networks, 148:48-65, 2022. 2022
Evolutionary Optimization of Deep Learning Activation Functions Garrett Bingham, William Macke, and Risto Miikkulainen In Genetic and Evolutionary Computation Conference (GECCO '20), 289-296, Cancun, Mexico, 20... 2020
Areas of Interest
Supervised Learning Evolutionary Computation Neuroevolution
UT Only: Advice • Calendar • Admin