no code implementations • 25 Sep 2023 • Qiaoling Yang, Linkai Luo, Haoyu Zhang, Hong Peng, Ziyang Chen
To address this, we propose a sample attention memory network (SAMN) that effectively combines SVM and NN by incorporating sample attention module, class prototypes, and memory block to NN.
no code implementations • 14 Jul 2023 • Linkai Luo, Qiaoling Yang, Hong Peng, Yiding Wang, Ziyang Chen
We first formulate the training and parameter selection of SVC as a minimax optimization problem named as MaxMin-L2-SVC-NCH, in which the minimization problem is an optimization problem of finding the closest points between two normal convex hulls (L2-SVC-NCH) while the maximization problem is an optimization problem of finding the optimal Gaussian kernel parameters.
no code implementations • 13 Jan 2022 • Zhimin Tang, Linkai Luo, Bike Xie, Yiyu Zhu, Rujie Zhao, Lvqing Bi, Chao Lu
In this work, we propose a new automatic pruning method - Sparse Connectivity Learning (SCL).
1 code implementation • 28 Sep 2019 • Jiao Xie, Shaohui Lin, Yichen Zhang, Linkai Luo
The large memory and computation consumption in convolutional neural networks (CNNs) has been one of the main barriers for deploying them on resource-limited systems.
no code implementations • 23 Jul 2019 • Linkai Luo, Yue Wang
This paper describes our approach to the EmotionX-2019, the shared task of SocialNLP 2019.
no code implementations • WS 2018 • Linkai Luo, Haiqin Yang, Francis Y. L. Chin
The BiLSTM exhibits the power of modeling the word dependencies, and extracting the most relevant features for emotion classification.
no code implementations • 19 Jun 2018 • Linkai Luo, Haiqing Yang, Francis Y. L. Chin
The BiLSTM exhibits the power of modeling the word dependencies, and extracting the most relevant features for emotion classification.