839 citations · 2.3k across the 47 of their papers we have counts for
7 papers · 1 filter
Communication Efficiency in Federated Learning: Achievements and Challenges
Osama Shahid, Seyedamin Pouriyeh, Reza M. Parizi +3
Federated Learning (FL) is known to perform Machine Learning tasks in a distributed manner. Over the years, this has become an emerging technology especially with various data prot…
A Comprehensive Survey on Graph Anomaly Detection with Deep Learning
Xiaoxiao Ma, Jia Wu, Shan Xue +5
Anomalies represent rare observations (e.g., data records or events) that deviate significantly from others. Over several decades, research on anomaly mining has received increasin…
Generative Adversarial Reward Learning for Generalized Behavior Tendency Inference
Xiaocong Chen, Lina Yao, Xianzhi Wang +3
Recent advances in reinforcement learning have inspired increasing interest in learning user modeling adaptively through dynamic interactions, e.g., in reinforcement learning based…
Deep Conversational Recommender Systems: A New Frontier for Goal-Oriented Dialogue Systems
Dai Hoang Tran, Quan Z. Sheng, Wei Emma Zhang +5
In recent years, the emerging topics of recommender systems that take advantage of natural language processing techniques have attracted much attention, and one of their applicatio…
DeepKey: An EEG and Gait Based Dual-Authentication System
Xiang Zhang, Lina Yao, Chaoran Huang +3
Biometric authentication involves various technologies to identify individuals by exploiting their unique, measurable physiological and behavioral characteristics. However, traditi…
Uncovering Locally Discriminative Structure for Feature Analysis
Sen Wang, Feiping Nie, Xiaojun Chang +3
Manifold structure learning is often used to exploit geometric information among data in semi-supervised feature learning algorithms. In this paper, we find that local discriminati…
Unsupervised Feature Analysis with Class Margin Optimization
Sen Wang, Feiping Nie, Xiaojun Chang +3
Unsupervised feature selection has been always attracting research attention in the communities of machine learning and data mining for decades. In this paper, we propose an unsupe…