167 citations · 643 across the 64 of their papers we have counts for
14 papers · 1 filter
Poisoning Evasion: Symbiotic Adversarial Robustness for Graph Neural Networks
Ege Erdogan, Simon Geisler, Stephan Günnemann
It is well-known that deep learning models are vulnerable to small input perturbations. Such perturbed instances are called adversarial examples. Adversarial examples are commonly…
On the Adversarial Robustness of Graph Contrastive Learning Methods
Filippo Guerranti, Zinuo Yi, Anna Starovoit +3
Contrastive learning (CL) has emerged as a powerful framework for learning representations of images and text in a self-supervised manner while enhancing model robustness against a…
Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning
Jianxiang Feng, Jongseok Lee, Simon Geisler +2
To facilitate reliable deployments of autonomous robots in the real world, Out-of-Distribution (OOD) detection capabilities are often required. A powerful approach for OOD detectio…
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…
Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework
Franziska Schwaiger, Andrea Matic, Karsten Roscher +1
The ability to detect learned objects regardless of their appearance is crucial for autonomous systems in real-world applications. Especially for detecting humans, which is often a…
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Xuan Zhang, Limei Wang, Jacob Helwig +60
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating…
Uncertainty Estimation for Molecules: Desiderata and Methods
Tom Wollschläger, Nicholas Gao, Bertrand Charpentier +2
Graph Neural Networks (GNNs) are promising surrogates for quantum mechanical calculations as they establish unprecedented low errors on collections of molecular dynamics (MD) traje…
Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions
Lukas Gosch, Simon Geisler, Daniel Sturm +3
Despite its success in the image domain, adversarial training did not (yet) stand out as an effective defense for Graph Neural Networks (GNNs) against graph structure perturbations…
MAGNet: Motif-Agnostic Generation of Molecules from Shapes
Leon Hetzel, Johanna Sommer, Bastian Rieck +2
Recent advances in machine learning for molecules exhibit great potential for facilitating drug discovery from in silico predictions. Most models for molecule generation rely on th…
Ewald-based Long-Range Message Passing for Molecular Graphs
Arthur Kosmala, Johannes Gasteiger, Nicholas Gao +1
Neural architectures that learn potential energy surfaces from molecular data have undergone fast improvement in recent years. A key driver of this success is the Message Passing N…
Generalizing Neural Wave Functions
Nicholas Gao, Stephan Günnemann
Recent neural network-based wave functions have achieved state-of-the-art accuracies in modeling ab-initio ground-state potential energy surface. However, these networks can only s…
Transformers Meet Directed Graphs
Simon Geisler, Yujia Li, Daniel Mankowitz +3
Transformers were originally proposed as a sequence-to-sequence model for text but have become vital for a wide range of modalities, including images, audio, video, and undirected…
Are Defenses for Graph Neural Networks Robust?
Felix Mujkanovic, Simon Geisler, Stephan Günnemann +1
A cursory reading of the literature suggests that we have made a lot of progress in designing effective adversarial defenses for Graph Neural Networks (GNNs). Yet, the standard met…
Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks
Yan Scholten, Jan Schuchardt, Simon Geisler +2
Randomized smoothing is one of the most promising frameworks for certifying the adversarial robustness of machine learning models, including Graph Neural Networks (GNNs). Yet, exis…