Pan Kessel

According to our database, Pan Kessel authored at least 17 papers between 2019 and 2024.

Collaborative distances:

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

2024

Diffeomorphic Counterfactuals With Generative Models.

Ann-Kathrin Dombrowski, Jan E. Gerken, Klaus-Robert Müller, Pan Kessel

IEEE Trans. Pattern Anal. Mach. Intell., May, 2024

Equivariant Neural Tangent Kernels.

Philipp Misof, Pan Kessel, Jan E. Gerken

CoRR, 2024

Emergent Equivariance in Deep Ensembles.

Jan E. Gerken, Pan Kessel

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Fast and unified path gradient estimators for normalizing flows.

Lorenz Vaitl, Ludwig Winkler, Lorenz Richter, Pan Kessel

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Batched Predictors Generalize within Distribution.

Andreas Loukas, Pan Kessel

CoRR, 2023

Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories.

Kim A. Nicoli, Christopher J. Anders, Tobias Hartung, Karl Jansen, Pan Kessel, Shinichi Nakajima

CoRR, 2023

Physics-Informed Bayesian Optimization of Variational Quantum Circuits.

Kim Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Stefan Kühn, Klaus-Robert Müller, Paolo Stornati, Pan Kessel, Shinichi Nakajima

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Gradients should stay on path: better estimators of the reverse- and forward KL divergence for normalizing flows.

Lorenz Vaitl, Kim A. Nicoli, Shinichi Nakajima, Pan Kessel

Mach. Learn. Sci. Technol., December, 2022

Towards robust explanations for deep neural networks.

Ann-Kathrin Dombrowski, Christopher J. Anders, Klaus-Robert Müller, Pan Kessel

Pattern Recognit., 2022

Path-Gradient Estimators for Continuous Normalizing Flows.

Lorenz Vaitl, Kim Andrea Nicoli, Shinichi Nakajima, Pan Kessel

Proceedings of the International Conference on Machine Learning, 2022

2021

Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse.

Kim A. Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Pan Kessel, Shinichi Nakajima, Paolo Stornati

CoRR, 2021

Deep learning for surrogate modelling of 2D mantle convection.

Siddhant Agarwal, Nicola Tosi, Pan Kessel, Doris Breuer, Grégoire Montavon

CoRR, 2021

2020

On Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models.

Kim A. Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Pan Kessel, Shinichi Nakajima, Paolo Stornati

CoRR, 2020

Fairwashing explanations with off-manifold detergent.

Christopher J. Anders, Plamen Pasliev, Ann-Kathrin Dombrowski, Klaus-Robert Müller, Pan Kessel

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Asymptotically Unbiased Generative Neural Sampling.

Kim A. Nicoli, Shinichi Nakajima, Nils Strodthoff, Wojciech Samek, Klaus-Robert Müller, Pan Kessel

CoRR, 2019

Comment on "Solving Statistical Mechanics Using VANs": Introducing saVANt - VANs Enhanced by Importance and MCMC Sampling.

Kim Nicoli, Pan Kessel, Nils Strodthoff, Wojciech Samek, Klaus-Robert Müller, Shinichi Nakajima

CoRR, 2019

Explanations can be manipulated and geometry is to blame.

Ann-Kathrin Dombrowski, Maximilian Alber, Christopher J. Anders, Marcel Ackermann, Klaus-Robert Müller, Pan Kessel

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019