2 citations · 2 across the 17 of their papers we have counts for
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Point Cloud Sequence Encoding for Material-conditioned Graph Network Simulators
Philipp Dahlinger, Balázs Gyenes, Niklas Freymuth +6
Graph Network Simulators (GNSs) have emerged as powerful surrogates for complex physics-based simulation, offering inherent differentiability and orders-of-magnitude speedups over…
Towards Near-Real-Time Telemetry-Aware Routing with Neural Routing Algorithms
Andreas Boltres, Niklas Freymuth, Benjamin Schichtholz +2
Routing algorithms are crucial for efficient computer network operations, and in many settings they must be able to react to traffic bursts within milliseconds. Live telemetry data…
SEAR: Sample Efficient Action Chunking Reinforcement Learning
C. F. Maximilian Nagy, Onur Celik, Emiliyan Gospodinov +4
Action chunking improves exploration and accelerates value propagation in long-horizon reinforcement learning, but naively applying off-policy methods to the temporally extended ac…
Bridge Matching Sampler: Scalable Sampling via Generalized Fixed-Point Diffusion Matching
Denis Blessing, Lorenz Richter, Julius Berner +2
Sampling from unnormalized densities using diffusion models has emerged as a powerful paradigm. However, while recent approaches that use least-squares `matching' objectives have i…
Can Neural Networks Provide Latent Embeddings for Telemetry-Aware Greedy Routing?
Andreas Boltres, Niklas Freymuth, Gerhard Neumann
Telemetry-Aware routing promises to increase efficacy and responsiveness to traffic surges in computer networks. Recent research leverages Machine Learning to deal with the complex…
Improving Long-Range Interactions in Graph Neural Simulators via Hamiltonian Dynamics
Tai Hoang, Alessandro Trenta, Alessio Gravina +4
Learning to simulate complex physical systems from data has emerged as a promising way to overcome the limitations of traditional numerical solvers, which often require prohibitive…