1 code implementation • 24 May 2024 • Cong Lu, Shengran Hu, Jeff Clune
Go-Explore is a powerful family of algorithms designed to solve hard-exploration problems, built on the principle of archiving discovered states, and iteratively returning to and exploring from the most promising states.
1 code implementation • NeurIPS 2023 • Shengran Hu, Jeff Clune
We hypothesize one reason for such cognitive deficiencies is that they lack the benefits of thinking in language and that we can improve AI agents by training them to think like humans do.
no code implementations • 8 Oct 2021 • Shengran Hu, Ran Cheng, Cheng He, Zhichao Lu, Jing Wang, Miao Zhang
For the goal of automated design of high-performance deep convolutional neural networks (CNNs), Neural Architecture Search (NAS) methodology is becoming increasingly important for both academia and industries. Due to the costly stochastic gradient descent (SGD) training of CNNs for performance evaluation, most existing NAS methods are computationally expensive for real-world deployments.
no code implementations • 27 Nov 2020 • Shengran Hu, Ran Cheng, Cheng He, Zhichao Lu
In the recent past, neural architecture search (NAS) has attracted increasing attention from both academia and industries.