10.1k citations · 12.9k across the 89 of their papers we have counts for
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Learning Private Neural Language Modeling with Attentive Aggregation
Shaoxiong Ji, Shirui Pan, Guodong Long +3
Mobile keyboard suggestion is typically regarded as a word-level language modeling problem. Centralized machine learning technique requires massive user data collected to train on,…
Distributionally Robust Semi-Supervised Learning for People-Centric Sensing
Kaixuan Chen, Lina Yao, Dalin Zhang +3
Semi-supervised learning is crucial for alleviating labelling burdens in people-centric sensing. However, human-generated data inherently suffer from distribution shift in semi-sup…
Universal Network Representation for Heterogeneous Information Networks
Ruiqi Hu, Celina Ping Yu, Sai-Fu Fung +3
Network representation aims to represent the nodes in a network as continuous and compact vectors, and has attracted much attention in recent years due to its ability to capture co…
NeuRec: On Nonlinear Transformation for Personalized Ranking
Shuai Zhang, Lina Yao, Aixin Sun +3
Modeling user-item interaction patterns is an important task for personalized recommendations. Many recommender systems are based on the assumption that there exists a linear relat…
Tensorized Self-Attention: Efficiently Modeling Pairwise and Global Dependencies Together
Tao Shen, Tianyi Zhou, Guodong Long +2
Neural networks equipped with self-attention have parallelizable computation, light-weight structure, and the ability to capture both long-range and local dependencies. Further, th…
Multi-modality Sensor Data Classification with Selective Attention
Xiang Zhang, Lina Yao, Chaoran Huang +4
Multimodal wearable sensor data classification plays an important role in ubiquitous computing and has a wide range of applications in scenarios from healthcare to entertainment. H…
Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling
Tao Shen, Tianyi Zhou, Guodong Long +2
Recurrent neural networks (RNN), convolutional neural networks (CNN) and self-attention networks (SAN) are commonly used to produce context-aware representations. RNN can capture l…
Adversarially Regularized Graph Autoencoder for Graph Embedding
Shirui Pan, Ruiqi Hu, Guodong Long +3
Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics. Most existing embedding algorithms typically focus on preserving the…
Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling
Tao Shen, Tianyi Zhou, Guodong Long +3
Many natural language processing tasks solely rely on sparse dependencies between a few tokens in a sentence. Soft attention mechanisms show promising performance in modeling local…