1 citations · 2 across the 2 of their papers we have counts for
5 papers · 1 filter
Decentralized Federated Policy Gradient with Byzantine Fault-Tolerance and Provably Fast Convergence
Philip Jordan, Florian Grötschla, Flint Xiaofeng Fan +1
In Federated Reinforcement Learning (FRL), agents aim to collaboratively learn a common task, while each agent is acting in its local environment without exchanging raw trajectorie…
Flood and Echo Net: Algorithmically Aligned GNNs that Generalize
Joël Mathys, Florian Grötschla, Kalyan Varma Nadimpalli +1
Most Graph Neural Networks follow the standard message-passing framework where, in each step, all nodes simultaneously communicate with each other. We want to challenge this paradi…
Graphtester: Exploring Theoretical Boundaries of GNNs on Graph Datasets
Eren Akbiyik, Florian Grötschla, Beni Egressy +1
Graph Neural Networks (GNNs) have emerged as a powerful tool for learning from graph-structured data. However, even state-of-the-art architectures have limitations on what structur…
Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors
Moritz Neun, Christian Eichenberger, Henry Martin +27
The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem…
Learning Graph Algorithms With Recurrent Graph Neural Networks
Florian Grötschla, Joël Mathys, Roger Wattenhofer
Classical graph algorithms work well for combinatorial problems that can be thoroughly formalized and abstracted. Once the algorithm is derived, it generalizes to instances of any…