Tom Wollschläger

According to our database, Tom Wollschläger authored at least 10 papers between 2022 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space.

Mohamed Amine Ketata, Nicholas Gao, Johanna Sommer, Tom Wollschläger, Stephan Günnemann

CoRR, 2024

Expressivity and Generalization: Fragment-Biases for Molecular GNNs.

Tom Wollschläger, Niklas Kemper, Leon Hetzel, Johanna Sommer, Stephan Günnemann

CoRR, 2024

Energy-based Epistemic Uncertainty for Graph Neural Networks.

Dominik Fuchsgruber, Tom Wollschläger, Stephan Günnemann

CoRR, 2024

Uncertainty for Active Learning on Graphs.

Dominik Fuchsgruber, Tom Wollschläger, Bertrand Charpentier, Antonio Oroz, Stephan Günnemann

CoRR, 2024

Attacking Large Language Models with Projected Gradient Descent.

Simon Geisler, Tom Wollschläger, M. H. I. Abdalla, Johannes Gasteiger, Stephan Günnemann

CoRR, 2024

2023

Expressivity of Graph Neural Networks Through the Lens of Adversarial Robustness.

Francesco Campi, Lukas Gosch, Tom Wollschläger, Yan Scholten, Stephan Günnemann

CoRR, 2023

Efficient MILP Decomposition in Quantum Computing for ReLU Network Robustness.

Nicola Franco, Tom Wollschläger, Benedikt Poggel, Stephan Günnemann, Jeanette Miriam Lorenz

Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2023

Uncertainty Estimation for Molecules: Desiderata and Methods.

Tom Wollschläger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Günnemann

Proceedings of the International Conference on Machine Learning, 2023

Localized Randomized Smoothing for Collective Robustness Certification.

Jan Schuchardt, Tom Wollschläger, Aleksandar Bojchevski, Stephan Günnemann

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Quantum Robustness Verification: A Hybrid Quantum-Classical Neural Network Certification Algorithm.

Nicola Franco, Tom Wollschläger, Nicholas Gao, Jeanette Miriam Lorenz, Stephan Günnemann

Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2022