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Found 14 papers, 5 papers with code
On Synthetic Data for Back Translation
Back translation (BT) is one of the most significant technologies in NMT research fields.
ImSimCSE: Improving Contrastive Learning for Sentence Embeddings from Two Perspectives
This paper aims to improve contrastive learning for sentence embeddings from two perspectives: handling dropout noise and addressing feature corruption.
mSHINE: A Multiple-meta-paths Simultaneous Learning Framework for Heterogeneous Information Network Embedding
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.
LA-HCN: Label-based Attention for Hierarchical Multi-label TextClassification Neural Network
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
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
apk2vec: Semi-supervised multi-view representation learning for profiling Android applications
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.)
graph2vec: Learning Distributed Representations of Graphs
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
Context-aware, Adaptive and Scalable Android Malware Detection through Online Learning (extended version)
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.
A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization
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.
Incremental Minimax Optimization based Fuzzy Clustering for Large Multi-view Data
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
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.
subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs
Also, we show that the subgraph vectors could be used for building a deep learning variant of Weisfeiler-Lehman graph kernel.
Adaptive and Scalable Android Malware Detection through Online Learning
In order to perform scalable detection and to adapt to the drift and evolution in malware population, an online passive-aggressive classifier is used.
Contextual Weisfeiler-Lehman Graph Kernel For Malware Detection
To address this, we develop the Contextual Weisfeiler-Lehman kernel (CWLK) which is capable of capturing both these types of information.
