no code implementations • EMNLP 2021 • Yonghao Liu, Renchu Guan, Fausto Giunchiglia, Yanchun Liang, Xiaoyue Feng
Text classification is a fundamental task with broad applications in natural language processing.
1 code implementation • 7 Feb 2024 • Yinghao Song, Zhiyuan Cao, Wanhong Xiang, Sifan Long, Bo Yang, Hongwei Ge, Yanchun Liang, Chunguo Wu
Second, the predicting model (PM) enhances the brightness of pseudo low-light images.
1 code implementation • 1 Aug 2023 • Yubin Xiao, Di Wang, Boyang Li, Huanhuan Chen, Wei Pang, Xuan Wu, Hao Li, Dong Xu, Yanchun Liang, You Zhou
The Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem with broad real-world applications.
1 code implementation • 25 Aug 2021 • Xuan Wu, Jizong Han, Di Wang, Pengyue Gao, Quanlong Cui, Liang Chen, Yanchun Liang, Han Huang, Heow Pueh Lee, Chunyan Miao, You Zhou, Chunguo Wu
While many Particle Swarm Optimization (PSO) algorithms only use fitness to assess the performance of particles, in this work, we adopt Surprisingly Popular Algorithm (SPA) as a complementary metric in addition to fitness.
no code implementations • 12 Mar 2021 • Xuan Wu, Linhan Jia, Xiuyi Zhang, Liang Chen, Yanchun Liang, You Zhou, Chunguo Wu
To evolve the architectures under the framework of CGP, the operations such as convolution are identified as the types of function nodes of CGP, and the evolutionary operations are designed based on Evolutionary Strategy.
1 code implementation • 7 Nov 2020 • Muhammad Hassan, Yan Wang, Di Wang, Daixi Li, Yanchun Liang, You Zhou, Dong Xu
We collected 100, 000 shoeprints of subjects ranging from 7 to 80 years old and used the data to develop a deep learning end-to-end model ShoeNet to analyze age-related patterns and predict age.
2 code implementations • 31 May 2020 • Liang Chen, Yanchun Liang, Xiaohu Shi, You Zhou, Chunguo Wu
Time Delay Neural Network (TDNN) is a well-performing structure for DNN-based speaker recognition systems.
no code implementations • 22 Nov 2016 • Xixun Lin, Yanchun Liang, Fausto Giunchiglia, Xiaoyue Feng, Renchu Guan
In this paper, we study the problem of how to better embed entities and relations of knowledge bases into different low-dimensional spaces by taking full advantage of the additional semantics of relation paths, and we propose a compositional learning model of relation path embedding (RPE).