Search Results for author:
Found 8 papers, 6 papers with code
CoRe-GD: A Hierarchical Framework for Scalable Graph Visualization with GNNs
We introduce a scalable Graph Neural Network (GNN) based Graph Drawing framework with sub-quadratic runtime that can learn to optimize stress.
SURF: A Generalization Benchmark for GNNs Predicting Fluid Dynamics
We propose SURF, a benchmark designed to test the $\textit{generalization}$ of learned graph-based fluid simulators.
Flood and Echo: Algorithmic Alignment of GNNs with Distributed Computing
However, this raises two core questions i) How can we enable nodes to gather the required information in a given graph ($\textit{information exchange}$), even if is far away and ii) How can we design an execution framework which enables this information exchange for extrapolation to larger graph sizes ($\textit{algorithmic alignment for extrapolation}$).
SALSA-CLRS: A Sparse and Scalable Benchmark for Algorithmic Reasoning
We introduce an extension to the CLRS algorithmic learning benchmark, prioritizing scalability and the utilization of sparse representations.
Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors
We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task.
Abstract Visual Reasoning Enabled by Language
While artificial intelligence (AI) models have achieved human or even superhuman performance in many well-defined applications, they still struggle to show signs of broad and flexible intelligence.
Learning Graph Algorithms With Recurrent Graph Neural Networks
In order to scale, we focus on a recurrent architecture design that can learn simple graph problems end to end on smaller graphs and then extrapolate to larger instances.
Hierarchical Graph Structures for Congestion and ETA Prediction
Traffic4cast is an annual competition to predict spatio temporal traffic based on real world data.
