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Found 8 papers, 1 papers with code
E2USD: Efficient-yet-effective Unsupervised State Detection for Multivariate Time Series
We propose E2USD that enables efficient-yet-accurate unsupervised MTS state detection.
Uncertainty Quantification for Traffic Forecasting: A Unified Approach
To achieve this, we develop Deep Spatio-Temporal Uncertainty Quantification (DeepSTUQ), which can estimate both aleatoric and epistemic uncertainty.
Learning Robust Variational Information Bottleneck with Reference
We propose a new approach to train a variational information bottleneck (VIB) that improves its robustness to adversarial perturbations.
Variational Information Bottleneck Model for Accurate Indoor Position Recognition
The encoder is to find a good representation in the input data for the learning task.
Multi-Task Variational Information Bottleneck
Multi-task learning (MTL) is an important subject in machine learning and artificial intelligence.
Supervised and Semi-supervised Deep Probabilistic Models for Indoor Positioning Problems
Predicting smartphone users location with WiFi fingerprints has been a popular research topic recently.
Convolutional Mixture Density Recurrent Neural Network for Predicting User Location with WiFi Fingerprints
Predicting smartphone users activity using WiFi fingerprints has been a popular approach for indoor positioning in recent years.
A Probabilistic Approach for Discovering Daily Human Mobility Patterns with Mobile Data
In the experiments, the effectiveness of our method is validated on the real user data collected from different users.
