1k citations · 4.2k across the 120 of their papers we have counts for
18 papers · 1 filter
Modeling Microenvironment Trajectories on Spatial Transcriptomics with NicheFlow
Kristiyan Sakalyan, Alessandro Palma, Filippo Guerranti +2
Understanding the evolution of cellular microenvironments in spatiotemporal data is essential for deciphering tissue development and disease progression. While experimental techniq…
Unexplored flaws in multiple-choice VQA make benchmarking unreliable
Fabio Rosenthal, Sebastian Schmidt, Thorsten Graf +3
Previous works identify sensitivity to option order as a key issue in multiple-choice VQA (MC-VQA) evaluation and propose protocols to mitigate this effect. We show that such mitig…
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…
What Expressivity Theory Misses: Message Passing Complexity for GNNs
Niklas Kemper, Tom Wollschläger, Stephan Günnemann
Expressivity theory, characterizing which graphs a GNN can distinguish, has become the predominant framework for analyzing GNNs, with new models striving for higher expressivity. H…
A Machine Learning Perspective on Automated Driving Corner Cases
Sebastian Schmidt, Julius Körner, Stephan Günnemann
For high-stakes applications, like autonomous driving, a safe operation is necessary to prevent harm, accidents, and failures. Traditionally, difficult scenarios have been categori…
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…
Predictive Feature Caching for Training-free Acceleration of Molecular Geometry Generation
Johanna Sommer, John Rachwan, Nils Fleischmann +2
Flow matching models generate high-fidelity molecular geometries but incur significant computational costs during inference, requiring hundreds of network evaluations. This inferen…
Energy-Weighted Flow Matching: Unlocking Continuous Normalizing Flows for Efficient and Scalable Boltzmann Sampling
Niclas Dern, Lennart Redl, Sebastian Pfister +3
Sampling from unnormalized target distributions, e.g.\ Boltzmann distributions , is fundamental to many scientific applications yet comp…
Enforcing Latent Euclidean Geometry in Single-Cell VAEs for Manifold Interpolation
Alessandro Palma, Sergei Rybakov, Leon Hetzel +2
Latent space interpolations are a powerful tool for navigating deep generative models in applied settings. An example is single-cell RNA sequencing, where existing methods model ce…
Model Collapse Is Not a Bug but a Feature in Machine Unlearning for LLMs
Yan Scholten, Sophie Xhonneux, Leo Schwinn +1
Current unlearning methods for LLMs optimize on the private information they seek to remove by incorporating it into their fine-tuning data. We argue this not only risks reinforcin…
Effective Data Pruning through Score Extrapolation
Sebastian Schmidt, Prasanga Dhungel, Christoffer Löffler +3
Training advanced machine learning models demands massive datasets, resulting in prohibitive computational costs. To address this challenge, data pruning techniques identify and re…
Joint Relational Database Generation via Graph-Conditional Diffusion Models
Mohamed Amine Ketata, David Lüdke, Leo Schwinn +1
Building generative models for relational databases (RDBs) is important for many applications, such as privacy-preserving data release and augmenting real datasets. However, most p…
Byte Pair Encoding for Efficient Time Series Forecasting
Leon Götz, Marcel Kollovieh, Stephan Günnemann +1
Existing time series tokenization methods predominantly encode a constant number of samples into individual tokens. This inflexible approach can generate excessive tokens for even…
LipShiFT: A Certifiably Robust Shift-based Vision Transformer
Rohan Menon, Nicola Franco, Stephan Günnemann
Deriving tight Lipschitz bounds for transformer-based architectures presents a significant challenge. The large input sizes and high-dimensional attention modules typically prove t…
Joint Out-of-Distribution Filtering and Data Discovery Active Learning
Sebastian Schmidt, Leonard Schenk, Leo Schwinn +1
As the data demand for deep learning models increases, active learning (AL) becomes essential to strategically select samples for labeling, which maximizes data efficiency and redu…
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…
Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting
Jan Schuchardt, Mina Dalirrooyfard, Jed Guzelkabaagac +3
Many forms of sensitive data, such as web traffic, mobility data, or hospital occupancy, are inherently sequential. The standard method for training machine learning models while e…
Unified Guidance for Geometry-Conditioned Molecular Generation
Sirine Ayadi, Leon Hetzel, Johanna Sommer +2
Effectively designing molecular geometries is essential to advancing pharmaceutical innovations, a domain, which has experienced great attention through the success of generative m…