1 code implementation • 17 Jun 2024 • Wenshuo Li, Xinghao Chen, Han Shu, Yehui Tang, Yunhe Wang
For instance, we achieve approximately $70\times$ compression for the Pythia-410M model, with the final performance being as accurate as the original model on various downstream tasks.
no code implementations • 24 May 2024 • Long Tan Le, Han Shu, Tung-Anh Nguyen, Choong Seon Hong, Nguyen H. Tran
While astonishingly capable, large Language Models (LLM) can sometimes produce outputs that deviate from human expectations.
no code implementations • 21 Mar 2024 • Han Shu, Jacob Mays
Based on the model and numerical tests we discuss several issues, including 1) establishing a valid counterfactual against which to measure benefits, 2) allocating cost to new and incumbent generators vs. solely allocating to loads, 3) calculating benefits at the portfolio vs. the individual project level, 4) identifying losers in a surplus-enhancing transmission expansion, and 5) quantifying the divergence between cost allocation decisions made ex ante and benefits realized ex post.
2 code implementations • 21 Dec 2023 • Han Shu, Wenshuo Li, Yehui Tang, Yiman Zhang, Yihao Chen, Houqiang Li, Yunhe Wang, Xinghao Chen
Massive following works have developed various applications based on the pre-trained SAM and achieved impressive performance on downstream vision tasks.
no code implementations • 19 Oct 2022 • Han Shu, Jacob Mays
Liberalized electricity markets often include resource adequacy mechanisms that require consumers to contract with generation resources well in advance of real-time operations.
4 code implementations • NeurIPS 2021 • Han Shu, Jiahao Wang, Hanting Chen, Lin Li, Yujiu Yang, Yunhe Wang
With the new operation, vision transformers constructed using additions can also provide powerful feature representations.
no code implementations • 19 Apr 2021 • Jiahao Wang, Han Shu, Weihao Xia, Yujiu Yang, Yunhe Wang
This paper studies the neural architecture search (NAS) problem for developing efficient generator networks.
1 code implementation • 3 Nov 2020 • Bochao Wang, Hang Xu, Jiajin Zhang, Chen Chen, Xiaozhi Fang, Yixing Xu, Ning Kang, Lanqing Hong, Chenhan Jiang, Xinyue Cai, Jiawei Li, Fengwei Zhou, Yong Li, Zhicheng Liu, Xinghao Chen, Kai Han, Han Shu, Dehua Song, Yunhe Wang, Wei zhang, Chunjing Xu, Zhenguo Li, Wenzhi Liu, Tong Zhang
Automated Machine Learning (AutoML) is an important industrial solution for automatic discovery and deployment of the machine learning models.
no code implementations • ECCV 2020 • Xinghao Chen, Yiman Zhang, Yunhe Wang, Han Shu, Chunjing Xu, Chang Xu
This paper proposes to learn a lightweight video style transfer network via knowledge distillation paradigm.
no code implementations • 7 Mar 2020 • Hanting Chen, Yunhe Wang, Han Shu, Changyuan Wen, Chunjing Xu, Boxin Shi, Chao Xu, Chang Xu
To promote the capability of student generator, we include a student discriminator to measure the distances between real images, and images generated by student and teacher generators.
no code implementations • 26 Feb 2020 • Han Shu, Yunhe Wang
Moreover, we transplant the searched network architecture to other datasets which are not involved in the architecture searching procedure.
no code implementations • 27 Jul 2019 • Kai Han, Yunhe Wang, Han Shu, Chuanjian Liu, Chunjing Xu, Chang Xu
This paper expands the strength of deep convolutional neural networks (CNNs) to the pedestrian attribute recognition problem by devising a novel attribute aware pooling algorithm.
2 code implementations • ICCV 2019 • Han Shu, Yunhe Wang, Xu Jia, Kai Han, Hanting Chen, Chunjing Xu, Qi Tian, Chang Xu
Generative adversarial networks (GANs) have been successfully used for considerable computer vision tasks, especially the image-to-image translation.