167 citations · 643 across the 64 of their papers we have counts for
70 papers
What Is a Clarification? Estimating Model-Dependent Input Ambiguity
Jingqin Tang, Tim Tomov, Sebastian Schmidt +2
Ambiguous inputs can admit multiple interpretations, each potentially yielding a different answer. Existing work largely treats this ambiguity as a property of the input, yet for a…
Physics-Aligned Electronic Ground-State Learning Improves Generalization
Eike S. Eberhard, Xaver Kainz, Viktor Kotsev +2
Machine-learned interatomic potentials (MLIPs) excel at in-distribution tasks, accelerating drug and material development, yet they struggle to generalize out-of-distribution. We p…
Activation Denoising: A Robustness View on Parallel vs Sequential LLM Quantization
Yan Scholten, Rachel Lawrence, James Hensman +3
Post-training quantization is a powerful tool for compressing large language models. The most scalable methods quantize every layer in parallel, but quantization errors then compou…
CDMD: A Cross-Dataset Mixed-Type Diffusion Model for Tabular Data
Mohamed Amine Ketata, Maximilian Schambach, Stephan Günnemann
Generative models for tabular data are typically trained separately for each dataset, limiting knowledge transfer and requiring the storage of many specialized models. In this pape…
Derivative Informed Learning of Exchange-Correlation Functionals
Eike S. Eberhard, Luca A. Thiede, Abdul Aldossary +5
Machine-learned (ML) exchange-correlation (XC) functionals aim to replace human-designed density functional approximations by learning directly from reference data, but they still…
Task-Aware Calibration: Provably Optimal Decoding in LLMs
Tim Tomov, Dominik Fuchsgruber, Rajeev Verma +1
LLM decoding often relies on the model's predictive distribution to generate an output. Consequently, misalignment with respect to the true generating distribution leads to subopti…
Transferable SCF-Acceleration through Solver-Aligned Initialization Learning
Eike S. Eberhard, Viktor Kotsev, Timm Güthle +1
The cost of Kohn-Sham density functional theory (KS-DFT) calculations scales with the number of solver iterations, which depends on the quality of the initial guess. Machine learni…
EgoDyn-Bench: Evaluating Ego-Motion Understanding in Vision-Centric Foundation Models for Autonomous Driving
Finn Rasmus Schäfer, Yuan Gao, Dingrui Wang +5
While Vision-Language Models (VLMs) have advanced high-level reasoning in autonomous driving, their ability to ground this reasoning in the underlying physics of ego-motion remains…
A Coin Flip for Safety: LLM Judges Fail to Reliably Measure Adversarial Robustness
Leo Schwinn, Moritz Ladenburger, Tim Beyer +3
Automated \enquote{LLM-as-a-Judge} frameworks have become the de facto standard for scalable evaluation across natural language processing. For instance, in safety evaluation, thes…
Excited Pfaffians: Generalized Neural Wave Functions Across Structure and State
Nicholas Gao, Till Grutschus, Frank Noé +1
Neural-network wave functions in Variational Monte Carlo (VMC) have achieved great success in accurately representing both ground and excited states. However, achieving sufficient…
A Scalable Multi-Task Model for Virtual Sensors
Leon Götz, Lars Frederik Peiss, Erik Sauer +4
Virtual sensors replace expensive physical sensors in critical applications through machine learning by predicting target signals from available measurements. Existing virtual sens…
The Illusion of Certainty: Uncertainty Quantification for LLMs Fails under Ambiguity
Tim Tomov, Dominik Fuchsgruber, Tom Wollschläger +1
Accurate uncertainty quantification (UQ) in Large Language Models (LLMs) is critical for trustworthy deployment. While real-world language is inherently ambiguous, reflecting aleat…
Diffusion LLMs are Natural Adversaries for any LLM
David Lüdke, Tom Wollschläger, Paul Ungermann +2
We introduce a novel framework that transforms the resource-intensive (adversarial) prompt optimization problem into an \emph{efficient, amortized inference task}. Our core insight…
Long-Range Graph Wavelet Networks
Filippo Guerranti, Fabrizio Forte, Simon Geisler +1
Modeling long-range interactions, the propagation of information across distant parts of a graph, is a central challenge in graph machine learning. Graph wavelets, inspired by mult…
Scalable Object Detection in the Car Interior With Vision Foundation Models
Sebastian Schmidt, Bálint Mészáros, Ahmet Firintepe +1
AI tasks in the car interior like identifying and localizing externally introduced objects is crucial for response quality of personal assistants. However, computational resources…
SAFT: Structure-Aware Fine-Tuning of LLMs for AMR-to-Text Generation
Rafiq Kamel, Filippo Guerranti, Simon Geisler +1
Large Language Models (LLMs) are increasingly applied to tasks involving structured inputs such as graphs. Abstract Meaning Representations (AMRs), which encode rich semantics as d…
Sampling-aware Adversarial Attacks Against Large Language Models
Tim Beyer, Yan Scholten, Leo Schwinn +1
To guarantee safe and robust deployment of large language models (LLMs) at scale, it is critical to accurately assess their adversarial robustness. Existing adversarial attacks typ…
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…
Uncertainty Estimation for Heterophilic Graphs Through the Lens of Information Theory
Dominik Fuchsgruber, Tom Wollschläger, Johannes Bordne +1
While uncertainty estimation for graphs recently gained traction, most methods rely on homophily and deteriorate in heterophilic settings. We address this by analyzing message pass…
Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems
Michael Scherbela, Nicholas Gao, Philipp Grohs +1
We present finite-range embeddings (FiRE), a novel wave function ansatz for accurate large-scale ab-initio electronic structure calculations. Compared to contemporary neural-networ…
OmegAMP: Targeted AMP Discovery via Biologically Informed Generation
Diogo Soares, Leon Hetzel, Paulina Szymczak +6
Deep learning-based antimicrobial peptide (AMP) discovery faces critical challenges such as limited controllability, lack of representations that efficiently model antimicrobial pr…
Prior2Former -- Evidential Modeling of Mask Transformers for Assumption-Free Open-World Panoptic Segmentation
Sebastian Schmidt, Julius Körner, Dominik Fuchsgruber +3
In panoptic segmentation, individual instances must be separated within semantic classes. As state-of-the-art methods rely on a pre-defined set of classes, they struggle with novel…
LLM-Safety Evaluations Lack Robustness
Tim Beyer, Sophie Xhonneux, Simon Geisler +3
In this paper, we argue that current safety alignment research efforts for large language models are hindered by many intertwined sources of noise, such as small datasets, methodol…
REINFORCE Adversarial Attacks on Large Language Models: An Adaptive, Distributional, and Semantic Objective
Simon Geisler, Tom Wollschläger, M. H. I. Abdalla +3
To circumvent the alignment of large language models (LLMs), current optimization-based adversarial attacks usually craft adversarial prompts by maximizing the likelihood of a so-c…