Been There, Done That: What Your Mobility Traces Reveal about Your Behavior

Mobility is a central aspect of our life; the locations we visit reflect our tastes and lifestyle and shape our social relationships. The ability to foresee the places a user will visit is therefore beneficial to numerous applications, ranging from forecasting the dynamics of crowds to improving the relevance of location-based recommendations. To solve the Next Place Prediction task of the Nokia Mobile Data Challenge, we developed several mobility predictors, based on graphical models, neural networks, and decision trees, and explain some of the challenges that we faced. Then, we combine these predictors using different blending strategies, which improve the prediction accuracy over any individual predictor.

Been There, Done That: What Your Mobility Traces Reveal about Your Behavior

Graph Chatbot

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks