#Mobile Phone Data #Privacy #Deep Learning

Generating synthetic mobility data for a realistic population with RNNs to improve utility and privacy

Authors: Alex Berke, Ronan Doorley, Kent Larson, Esteban Moro Publication: arXiv preprint arXiv:2201.01139 (2022). Link Abstract: Location data collected from mobile devices represent mobility behaviors at individual and societal levels. These data have important applications ranging from transportation planning to epidemic modeling. However, issues must be overcome to best serve these use cases: The data often represent a limited sample of the population and use of the data jeopardizes privacy. ...

#Deep Learning #Networks

How to Organize your Deep Reinforcement Learning Agents: The Importance of Communication Topology

Authors: Dhaval Adjodah, Dan Calacci, Abhimanyu Dubey, Peter Krafft, Esteban Moro, Alex `Sandy' Pentland Journal: Preprint (2018). arXiv Abstract: In this empirical paper, we investigate how learning agents can be arranged in more efficient communication topologies for improved learning. This is an important problem because a common technique to improve speed and robustness of learning in deep reinforcement learning and many other machine learning algorithms is to run multiple learning agents in parallel. ...

#Memes #Deep Learning #Image processing

MemeSequencer: Sparse Matching for Embedding Image Macros

Authors: Abhimanyu Dubey, Esteban Moro, Manuel Cebrian, Iyad Rahwan Journal: WWW'18 Proceedings of the Web Conference 2018 LINK Abstract: The analysis of the creation, mutation, and propagation of social media content on the Internet is an essential problem in computational social science, affecting areas ranging from marketing to political mobilization. A first step towards understanding the evolution of images online is the analysis of rapidly modifying and propagating memetic imagery or ‘memes’. ...