no code implementations • 29 May 2023 • Kang Yang, Kunhao Lai
Deep neural network (DNN) models have become a critical asset of the model owner as training them requires a large amount of resource (i. e. labeled data).
no code implementations • 27 May 2023 • Di Liu, Sebastian Mair, Kang Yang, Simone Baldi, Paolo Frasca, Matthias Althoff
We show that self-organization promotes resilience to acceleration limits and communication failures, i. e., homogenizing to a common group behavior makes the platoon recover from these causes of impairments.
no code implementations • 26 Apr 2023 • Chunxi Guo, Zhiliang Tian, Jintao Tang, Pancheng Wang, Zhihua Wen, Kang Yang, Ting Wang
Text-to-SQL is a task that converts a natural language question into a structured query language (SQL) to retrieve information from a database.
1 code implementation • Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence 2022 • Xiang Huang, Hongsheng Liu, Beiji Shi, Zidong Wang, Kang Yang, Yang Li, Min Wang, Haotian Chu, Jing Zhou, Fan Yu, Bei Hua, Bin Dong, Lei Chen
In recent years, deep learning technology has been used to solve partial differential equations (PDEs), among which the physics-informed neural networks (PINNs)method emerges to be a promising method for solving both forward and inverse PDE problems.
no code implementations • 25 Apr 2022 • Wei Liu, Tao Zhang, Rui Wang, Kaiwen Li, Wenhua Li, Kang Yang
A dynamic pointer network (DYPN) is introduced as the TSP solver, which takes city locations as inputs and immediately outputs a permutation of nodes.
no code implementations • 8 Feb 2022 • Guhong Nie, Lirui Xiao, Menglong Zhu, Dongliang Chu, Yue Shen, Peng Li, Kang Yang, Li Du, Bo Chen
For binary neural networks (BNNs) to become the mainstream on-device computer vision algorithm, they must achieve a superior speed-vs-accuracy tradeoff than 8-bit quantization and establish a similar degree of general applicability in vision tasks.
no code implementations • 15 Nov 2021 • Xiang Huang, Zhanhong Ye, Hongsheng Liu, Beiji Shi, Zidong Wang, Kang Yang, Yang Li, Bingya Weng, Min Wang, Haotian Chu, Fan Yu, Bei Hua, Lei Chen, Bin Dong
Many important problems in science and engineering require solving the so-called parametric partial differential equations (PDEs), i. e., PDEs with different physical parameters, boundary conditions, shapes of computation domains, etc.
no code implementations • 2 Nov 2021 • Xiang Huang, Hongsheng Liu, Beiji Shi, Zidong Wang, Kang Yang, Yang Li, Bingya Weng, Min Wang, Haotian Chu, Jing Zhou, Fan Yu, Bei Hua, Lei Chen, Bin Dong
In recent years, deep learning technology has been used to solve partial differential equations (PDEs), among which the physics-informed neural networks (PINNs) emerges to be a promising method for solving both forward and inverse PDE problems.
1 code implementation • 18 Jun 2021 • Qigong Sun, Xiufang Li, Fanhua Shang, Hongying Liu, Kang Yang, Licheng Jiao, Zhouchen Lin
The training of deep neural networks (DNNs) always requires intensive resources for both computation and data storage.
no code implementations • 6 Nov 2020 • Yangchun Yan, Rongzuo Guo, Chao Li, Kang Yang, Yongjun Xu
However, these methods ignore a small part of weights in the next layer which disappears as the feature map is removed.
no code implementations • 8 Jul 2020 • Kang Yang, Siddhardh C. Morampudi, Emil J. Bergholtz
We establish the appearance of a qualitatively new type of spin liquid with emergent exceptional points when coupling to the environment.
Strongly Correlated Electrons Mesoscale and Nanoscale Physics Quantum Physics
no code implementations • 23 Jun 2020 • Jianrong Xu, Boyu Diao, Bifeng Cui, Kang Yang, Chao Li, Yongjun Xu
Deep learning has achieved impressive results in many areas, but the deployment of edge intelligent devices is still very slow.
no code implementations • 31 May 2019 • Qigong Sun, Fanhua Shang, Kang Yang, Xiufang Li, Yan Ren, Licheng Jiao
The training of deep neural networks (DNNs) requires intensive resources both for computation and for storage performance.
no code implementations • 20 Mar 2019 • Peng Bao, Wenjun Xia, Kang Yang, Weiyan Chen, Mianyi Chen, Yan Xi, Shanzhou Niu, Jiliu Zhou, He Zhang, Huaiqiang Sun, Zhangyang Wang, Yi Zhang
Over the past few years, dictionary learning (DL)-based methods have been successfully used in various image reconstruction problems.
no code implementations • 15 Oct 2018 • Peng Bao, Wenjun Xia, Kang Yang, Jiliu Zhou, Yi Zhang
Traditional dictionary learning based CT reconstruction methods are patch-based and the features learned with these methods often contain shifted versions of the same features.
no code implementations • 8 Oct 2018 • Qigong Sun, Fanhua Shang, Xiufang Li, Kang Yang, Peizhuo Lv, Licheng Jiao
Deep neural networks require extensive computing resources, and can not be efficiently applied to embedded devices such as mobile phones, which seriously limits their applicability.