11 citations · 18 across the 6 of their papers we have counts for
10 papers
Exploring Molecule Generation Using Latent Space Graph Diffusion
Prashanth Pombala, Gerrit Grossmann, Verena Wolf
Generating molecular graphs is a challenging task due to their discrete nature and the competitive objectives involved. Diffusion models have emerged as SOTA approaches in data gen…
Enhancing GNNs with Architecture-Agnostic Graph Transformations: A Systematic Analysis
Zhifei Li, Gerrit Großmann, Verena Wolf
In recent years, a wide variety of graph neural network (GNN) architectures have emerged, each with its own strengths, weaknesses, and complexities. Various techniques, including r…
GINA: Neural Relational Inference From Independent Snapshots
Gerrit Großmann, Julian Zimmerlin, Michael Backenköhler +1
Dynamical systems in which local interactions among agents give rise to complex emerging phenomena are ubiquitous in nature and society. This work explores the problem of inferring…
Abstraction-Guided Truncations for Stationary Distributions of Markov Population Models
Michael Backenköhler, Luca Bortolussi, Gerrit Großmann +1
To understand the long-run behavior of Markov population models, the computation of the stationary distribution is often a crucial part. We propose a truncation-based approximation…
Analysis of Markov Jump Processes under Terminal Constraints
Michael Backenköhler, Luca Bortolussi, Gerrit Großmann +1
Many probabilistic inference problems such as stochastic filtering or the computation of rare event probabilities require model analysis under initial and terminal constraints. We…
Rejection-Based Simulation of Non-Markovian Agents on Complex Networks
Gerrit Großmann, Luca Bortolussi, Verena Wolf
Stochastic models in which agents interact with their neighborhood according to a network topology are a powerful modeling framework to study the emergence of complex dynamic patte…