no code implementations • 3 Jun 2024 • Lifeng Shen, Jincheng Yu, Hansi Yang, James T. Kwok
Mixup and its variants form a popular class of data augmentation techniques. Using a random sample pair, it generates a new sample by linear interpolation of the inputs and labels.
3 code implementations • 30 Sep 2023 • Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Yue Wu, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, Zhenguo Li
We hope PIXART-$\alpha$ will provide new insights to the AIGC community and startups to accelerate building their own high-quality yet low-cost generative models from scratch.
1 code implementation • 21 Sep 2023 • Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T. Kwok, Zhenguo Li, Adrian Weller, Weiyang Liu
Our MetaMath-7B model achieves 66. 4% on GSM8K and 19. 4% on MATH, exceeding the state-of-the-art models of the same size by 11. 5% and 8. 7%.
Ranked #57 on Arithmetic Reasoning on GSM8K (using extra training data)
no code implementations • 5 Jul 2023 • Jingwei Zhang, Han Shi, Jincheng Yu, Enze Xie, Zhenguo Li
Generative models can be categorized into two types: explicit generative models that define explicit density forms and allow exact likelihood inference, such as score-based diffusion models (SDMs) and normalizing flows; implicit generative models that directly learn a transformation from the prior to the data distribution, such as generative adversarial nets (GANs).
no code implementations • 18 Nov 2020 • Feng Gao, Jincheng Yu, Hao Shen, Yu Wang, Huazhong Yang
Learning depth and ego-motion from unlabeled videos via self-supervision from epipolar projection can improve the robustness and accuracy of the 3D perception and localization of vision-based robots.
no code implementations • 14 May 2018 • Wenshuo Li, Jincheng Yu, Xuefei Ning, Pengjun Wang, Qi Wei, Yu Wang, Huazhong Yang
So, in this paper, we propose a hardware-software collaborative attack framework to inject hidden neural network Trojans, which works as a back-door without requiring manipulating input images and is flexible for different scenarios.
no code implementations • 24 Dec 2017 • Kaiyuan Guo, Shulin Zeng, Jincheng Yu, Yu Wang, Huazhong Yang
Various FPGA based accelerator designs have been proposed with software and hardware optimization techniques to achieve high speed and energy efficiency.
Hardware Architecture