87 citations · 197 across the 12 of their papers we have counts for
5 papers · 1 filter
Differentiable Architecture Search for Reinforcement Learning
Yingjie Miao, Xingyou Song, John D. Co-Reyes +4
In this paper, we investigate the fundamental question: To what extent are gradient-based neural architecture search (NAS) techniques applicable to RL? Using the original DARTS as…
Rethinking Co-design of Neural Architectures and Hardware Accelerators
Yanqi Zhou, Xuanyi Dong, Berkin Akin +7
Neural architectures and hardware accelerators have been two driving forces for the progress in deep learning. Previous works typically attempt to optimize hardware given a fixed m…
PyGlove: Symbolic Programming for Automated Machine Learning
Daiyi Peng, Xuanyi Dong, Esteban Real +7
Neural networks are sensitive to hyper-parameter and architecture choices. Automated Machine Learning (AutoML) is a promising paradigm for automating these choices. Current ML soft…
ES-ENAS: Efficient Evolutionary Optimization for Large Hybrid Search Spaces
Xingyou Song, Krzysztof Choromanski, Jack Parker-Holder +8
In this paper, we approach the problem of optimizing blackbox functions over large hybrid search spaces consisting of both combinatorial and continuous parameters. We demonstrate t…
Evolving Reinforcement Learning Algorithms
John D. Co-Reyes, Yingjie Miao, Daiyi Peng +5
We propose a method for meta-learning reinforcement learning algorithms by searching over the space of computational graphs which compute the loss function for a value-based model-…