43 citations · 64 across the 7 of their papers we have counts for
7 papers
Uncertainty Quantification for Traffic Forecasting: A Unified Approach
Weizhu Qian, Dalin Zhang, Yan Zhao +2
Uncertainty is an essential consideration for time series forecasting tasks. In this work, we specifically focus on quantifying the uncertainty of traffic forecasting. To achieve t…
Learning Robust Variational Information Bottleneck with Reference
Weizhu Qian, Bowei Chen, Xiaowei Huang
We propose a new approach to train a variational information bottleneck (VIB) that improves its robustness to adversarial perturbations. Unlike the traditional methods where the ha…
Variational Information Bottleneck Model for Accurate Indoor Position Recognition
Weizhu Qian, Franck Gechter
Recognizing user location with WiFi fingerprints is a popular approach for accurate indoor positioning problems. In this work, our goal is to interpret WiFi fingerprints into actua…
Multi-Task Variational Information Bottleneck
Weizhu Qian, Bowei Chen, Yichao Zhang +2
Multi-task learning (MTL) is an important subject in machine learning and artificial intelligence. Its applications to computer vision, signal processing, and speech recognition ar…
A Probabilistic Approach for Discovering Daily Human Mobility Patterns with Mobile Data
Weizhu Qian, Fabrice Lauri, Franck Gechter
Discovering human mobility patterns with geo-location data collected from smartphone users has been a hot research topic in recent years. In this paper, we attempt to discover dail…
Convolutional Mixture Density Recurrent Neural Network for Predicting User Location with WiFi Fingerprints
Weizhu Qian, Fabrice Lauri, Franck Gechter
Predicting smartphone users activity using WiFi fingerprints has been a popular approach for indoor positioning in recent years. However, such a high dimensional time-series predic…
Supervised and Semi-supervised Deep Probabilistic Models for Indoor Positioning Problems
Weizhu Qian, Fabrice Lauri, Franck Gechter
Predicting smartphone users location with WiFi fingerprints has been a popular research topic recently. In this work, we propose two novel deep learning-based models, the convoluti…