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Found 7 papers, 5 papers with code
Laying the Foundations of Deep Long-Term Crowd Flow Prediction
Predicting the crowd behavior in complex environments is a key requirement for crowd and disaster management, architectural design, and urban planning.
Hopper: Multi-hop Transformer for Spatiotemporal Reasoning
We evaluate over CATER dataset and find that Hopper achieves 73. 2% Top-1 accuracy using just 1 FPS by hopping through just a few critical frames.
Graph-Based Generative Representation Learning of Semantically and Behaviorally Augmented Floorplans
We present a floorplan embedding technique that uses an attributed graph to represent the geometric information as well as design semantics and behavioral features of the inhabitants as node and edge attributes.
GitEvolve: Predicting the Evolution of GitHub Repositories
Given its crucial role, there is a need to better understand and model the dynamics of GitHub as a social platform.
Understanding Echo Chambers in E-commerce Recommender Systems
Current research on recommender systems mostly focuses on matching users with proper items based on user interests.
HID: Hierarchical Multiscale Representation Learning for Information Diffusion
In this paper, we present a Hierarchical Information Diffusion (HID) framework by integrating user representation learning and multiscale modeling.
Deep Crowd-Flow Prediction in Built Environments
In this paper, we propose an approach to instantly predict the long-term flow of crowds in arbitrarily large, realistic environments.