17 citations · 36 across the 11 of their papers we have counts for
7 papers · 1 filter
Customizing Graph Neural Networks using Path Reweighting
Jianpeng Chen, Yujing Wang, Ming Zeng +5
Graph Neural Networks (GNNs) have been extensively used for mining graph-structured data with impressive performance. However, because these traditional GNNs do not distinguish amo…
Influence Diagram Bandits: Variational Thompson Sampling for Structured Bandit Problems
Tong Yu, Branislav Kveton, Zheng Wen +2
We propose a novel framework for structured bandits, which we call an influence diagram bandit. Our framework captures complex statistical dependencies between actions, latent vari…
Customized Graph Embedding: Tailoring Embedding Vectors to different Applications
Bitan Hou, Yujing Wang, Ming Zeng +4
Graph is a natural representation of data for a variety of real-word applications, such as knowledge graph mining, social network analysis and biological network comparison. For th…
Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention
Ming Zeng, Haoxiang Gao, Tong Yu +4
Deep neural networks, including recurrent networks, have been successfully applied to human activity recognition. Unfortunately, the final representation learned by recurrent netwo…
Semi-Supervised Convolutional Neural Networks for Human Activity Recognition
Ming Zeng, Tong Yu, Xiao Wang +3
Labeled data used for training activity recognition classifiers are usually limited in terms of size and diversity. Thus, the learned model may not generalize well when used in rea…
SpectralLeader: Online Spectral Learning for Single Topic Models
Tong Yu, Branislav Kveton, Zheng Wen +2
We study the problem of learning a latent variable model from a stream of data. Latent variable models are popular in practice because they can explain observed data in terms of un…
Interpretable Categorization of Heterogeneous Time Series Data
Ritchie Lee, Mykel J. Kochenderfer, Ole J. Mengshoel +1
Understanding heterogeneous multivariate time series data is important in many applications ranging from smart homes to aviation. Learning models of heterogeneous multivariate time…