Search Results for author: Lihui Chen

Found 14 papers, 5 papers with code

ImSimCSE: Improving Contrastive Learning for Sentence Embeddings from Two Perspectives

no code implementations22 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.

Contrastive Learning Sentence Embeddings

mSHINE: A Multiple-meta-paths Simultaneous Learning Framework for Heterogeneous Information Network Embedding

1 code implementation6 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.

Link Prediction Network Embedding +1

LA-HCN: Label-based Attention for Hierarchical Multi-label TextClassification Neural Network

no code implementations23 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

Capsule Graph Neural Network

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.

Graph Classification

apk2vec: Semi-supervised multi-view representation learning for profiling Android applications

no code implementations15 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.)

Clone Detection Malware Analysis +2

graph2vec: Learning Distributed Representations of Graphs

5 code implementations17 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.

General Classification Graph Classification +2

Context-aware, Adaptive and Scalable Android Malware Detection through Online Learning (extended version)

no code implementations3 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.

Android Malware Detection Malware Detection

A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization

no code implementations6 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.

Android Malware Detection Malware Detection +1

Incremental Minimax Optimization based Fuzzy Clustering for Large Multi-view Data

no code implementations25 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.

Multi-View Fuzzy Clustering with Minimax Optimization for Effective Clustering of Data from Multiple Sources

no code implementations25 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.

Adaptive and Scalable Android Malware Detection through Online Learning

no code implementations23 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.

Android Malware Detection BIG-bench Machine Learning +1

Contextual Weisfeiler-Lehman Graph Kernel For Malware Detection

no code implementations21 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.

Malware Detection

Cannot find the paper you are looking for? You can Submit a new open access paper.