no code implementations • 8 Jun 2023 • Yiping Tang, Kohei Hatano, Eiji Takimoto
Some previous work proposes to transform neural networks into equivalent Boolean expressions and apply verification techniques for characteristics of interest.
1 code implementation • 31 Oct 2020 • Chen Wei, Yiping Tang, Chuang Niu, Haihong Hu, Yue Wang, Jimin Liang
To enhance the predictive performance of neural predictors, we devise two self-supervised learning methods from different perspectives to pre-train the architecture embedding part of neural predictors to generate a meaningful representation of neural architectures.
1 code implementation • 28 Mar 2020 • Chen Wei, Chuang Niu, Yiping Tang, Yue Wang, Haihong Hu, Jimin Liang
In this paper, we propose a neural predictor guided evolutionary algorithm to enhance the exploration ability of EA for NAS (NPENAS) and design two kinds of neural predictors.
no code implementations • 18 Oct 2019 • Yiping Tang, Chuang Niu, Minghao Dong, Shenghan Ren, Jimin Liang
Many of the state-of-the-art methods predict the boundaries of action instances based on predetermined anchors akin to the two-dimensional object detection detectors.