17 citations · 36 across the 11 of their papers we have counts for
12 papers
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…
Adaptive Stress Testing: Finding Likely Failure Events with Reinforcement Learning
Ritchie Lee, Ole J. Mengshoel, Anshu Saksena +5
Finding the most likely path to a set of failure states is important to the analysis of safety-critical systems that operate over a sequence of time steps, such as aircraft collisi…
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…
CADDeLaG: Framework for distributed anomaly detection in large dense graph sequences
Aniruddha Basak, Kamalika Das, Ole J. Mengshoel
Random walk based distance measures for graphs such as commute-time distance are useful in a variety of graph algorithms, such as clustering, anomaly detection, and creating low di…
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…
Customized Nonlinear Bandits for Online Response Selection in Neural Conversation Models
Bing Liu, Tong Yu, Ian Lane +1
Dialog response selection is an important step towards natural response generation in conversational agents. Existing work on neural conversational models mainly focuses on offline…
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…
Self-Awareness of Cloud Applications
Alexandru Iosup, Xiaoyun Zhu, Arif Merchant +6
Cloud applications today deliver an increasingly larger portion of the Information and Communication Technology (ICT) services. To address the scale, growth, and reliability of clo…
Belief Propagation by Message Passing in Junction Trees: Computing Each Message Faster Using GPU Parallelization
Lu Zheng, Ole Mengshoel, Jike Chong
Compiling Bayesian networks (BNs) to junction trees and performing belief propagation over them is among the most prominent approaches to computing posteriors in BNs. However, beli…