1 code implementation • NAACL 2022 • Jiahao Xu, Yubin Ruan, Wei Bi, Guoping Huang, Shuming Shi, Lihui Chen, Lemao Liu
Back translation (BT) is one of the most significant technologies in NMT research fields.
no code implementations • 22 May 2023 • Jiahao Xu, Wei Shao, Lihui Chen, Lemao Liu
This paper aims to improve contrastive learning for sentence embeddings from two perspectives: handling dropout noise and addressing feature corruption.
1 code implementation • 6 Apr 2021 • Xinyi Zhang, Lihui Chen
To address this issue, a novel meta-path-based HIN representation learning framework named mSHINE is designed to simultaneously learn multiple node representations for different meta-paths.
no code implementations • 23 Sep 2020 • Xinyi Zhang, Jiahao Xu, Charlie Soh, Lihui Chen
In this paper, we propose a Label-based Attention for Hierarchical Mutlti-label Text Classification Neural Network (LA-HCN), where the novel label-based attention module is designed to hierarchically extract important information from the text based on the labels from different hierarchy levels.
Multi Label Text Classification Multi-Label Text Classification +1
1 code implementation • ICLR 2019 • Zhang Xinyi, Lihui Chen
The high-quality node embeddings learned from the Graph Neural Networks (GNNs) have been applied to a wide range of node-based applications and some of them have achieved state-of-the-art (SOTA) performance.
Ranked #4 on Graph Classification on RE-M5K
no code implementations • 15 Sep 2018 • Annamalai Narayanan, Charlie Soh, Lihui Chen, Yang Liu, Lipo Wang
Building behavior profiles of Android applications (apps) with holistic, rich and multi-view information (e. g., incorporating several semantic views of an app such as API sequences, system calls, etc.)
5 code implementations • 17 Jul 2017 • Annamalai Narayanan, Mahinthan Chandramohan, Rajasekar Venkatesan, Lihui Chen, Yang Liu, Shantanu Jaiswal
Recent works on representation learning for graph structured data predominantly focus on learning distributed representations of graph substructures such as nodes and subgraphs.
Ranked #1 on Malware Detection on Android Malware Dataset
no code implementations • 3 Jun 2017 • Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, Yang Liu
Contrary to this fact, most of the prior works on Machine Learning based Android malware detection have assumed that the distribution of the observed malware characteristics (i. e., features) does not change over time.
no code implementations • 6 Apr 2017 • Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, Yang Liu
Most of the existing malware detection approaches use only one (or a selected few) of the aforementioned feature sets which prevent them from detecting a vast majority of attacks.
no code implementations • 25 Aug 2016 • Yangtao Wang, Lihui Chen, Xiao-Li Li
The detailed problem formulation, updating rules derivation, and the in-depth analysis of the proposed IminimaxFCM are provided.
no code implementations • 25 Aug 2016 • Yangtao Wang, Lihui Chen
It becomes important nowadays because more and more data can be collected in a variety of ways, in different settings and from different sources, so each data set can be represented by different sets of features to form different views of it.
2 code implementations • 29 Jun 2016 • Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, Yang Liu, Santhoshkumar Saminathan
Also, we show that the subgraph vectors could be used for building a deep learning variant of Weisfeiler-Lehman graph kernel.
no code implementations • 23 Jun 2016 • Annamalai Narayanan, Liu Yang, Lihui Chen, Liu Jinliang
In order to perform scalable detection and to adapt to the drift and evolution in malware population, an online passive-aggressive classifier is used.
no code implementations • 21 Jun 2016 • Annamalai Narayanan, Guozhu Meng, Liu Yang, Jinliang Liu, Lihui Chen
To address this, we develop the Contextual Weisfeiler-Lehman kernel (CWLK) which is capable of capturing both these types of information.