11 citations · 28 across the 12 of their papers we have counts for
9 papers · 1 filter
Discrete Bayesian Sample Inference for Graph Generation
Ole Petersen, Marcel Kollovieh, Marten Lienen +1
Generating graph-structured data is crucial in applications such as molecular generation, knowledge graphs, and network analysis. However, their discrete, unordered nature makes th…
Edit-Based Flow Matching for Temporal Point Processes
David Lüdke, Marten Lienen, Marcel Kollovieh +1
Temporal point processes (TPPs) are a fundamental tool for modeling event sequences in continuous time, but most existing approaches rely on autoregressive parameterizations that a…
UnHiPPO: Uncertainty-aware Initialization for State Space Models
Marten Lienen, Abdullah Saydemir, Stephan Günnemann
State space models are emerging as a dominant model class for sequence problems with many relying on the HiPPO framework to initialize their dynamics. However, HiPPO fundamentally…
Generative Modeling with Bayesian Sample Inference
Marten Lienen, Marcel Kollovieh, Stephan Günnemann
We present a novel view of diffusion-like generative modeling from the perspective of iterative Gaussian posterior inference. By treating the generated sample as an unknown variabl…
Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting
Marcel Kollovieh, Marten Lienen, David Lüdke +2
Recent advancements in generative modeling, particularly diffusion models, have opened new directions for time series modeling, achieving state-of-the-art performance in forecastin…
Add and Thin: Diffusion for Temporal Point Processes
David Lüdke, Marin Biloš, Oleksandr Shchur +2
Autoregressive neural networks within the temporal point process (TPP) framework have become the standard for modeling continuous-time event data. Even though these models can expr…
torchode: A Parallel ODE Solver for PyTorch
Marten Lienen, Stephan Günnemann
We introduce an ODE solver for the PyTorch ecosystem that can solve multiple ODEs in parallel independently from each other while achieving significant performance gains. Our imple…
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks
Marten Lienen, Stephan Günnemann
We propose a new method for spatio-temporal forecasting on arbitrarily distributed points. Assuming that the observed system follows an unknown partial differential equation, we de…
Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More
Johannes Gasteiger, Marten Lienen, Stephan Günnemann
The current best practice for computing optimal transport (OT) is via entropy regularization and Sinkhorn iterations. This algorithm runs in quadratic time as it requires the full…