Search Results for author:
Found 12 papers, 2 papers with code
Human mobility is well described by closed-form gravity-like models learned automatically from data
At the other end, we have complex machine learning and deep learning models, with tens of features and thousands of parameters, which predict mobility more accurately than gravity models at the cost of not being interpretable and not providing insight on human behavior.
Adverse weather amplifies social media activity
Weather that is adverse on both the temperature and precipitation dimensions produces markedly larger increases in social media activity.
The unequal effects of the health-economy tradeoff during the COVID-19 pandemic
The potential tradeoff between health outcomes and economic impact has been a major challenge in the policy making process during the COVID-19 pandemic.
Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics
Urbanization and its problems require an in-depth and comprehensive understanding of urban dynamics, especially the complex and diversified lifestyles in modern cities.
Generating synthetic mobility data for a realistic population with RNNs to improve utility and privacy
Related generative approaches have not solved the challenges of capturing both the patterns and variability in individuals' mobility behaviors over longer time periods, while also balancing the generation of realistic data with privacy.
Leveraging Communication Topologies Between Learning Agents in Deep Reinforcement Learning
A common technique to improve learning performance in deep reinforcement learning (DRL) and many other machine learning algorithms is to run multiple learning agents in parallel.
How to Organize your Deep Reinforcement Learning Agents: The Importance of Communication Topology
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.
Attack Tolerance of Link Prediction Algorithms: How to Hide Your Relations in a Social Network
Link prediction is one of the fundamental research problems in network analysis.
Social and Information Networks Cryptography and Security 91D30 (Primary) 68T20 (Secondary) G.2.2; J.4
MemeSequencer: Sparse Matching for Embedding Image Macros
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.
Improved Learning in Evolution Strategies via Sparser Inter-Agent Network Topologies
We draw upon a previously largely untapped literature on human collective intelligence as a source of inspiration for improving deep learning.
Weather impacts expressed sentiment
We conduct the largest ever investigation into the relationship between meteorological conditions and the sentiment of human expressions.
The Minority Game: an introductory guide
The Minority Game is a simple model for the collective behavior of agents in an idealized situation where they have to compete through adaptation for a finite resource.
Disordered Systems and Neural Networks Statistical Mechanics
