Kristof Schütt

According to our database, Kristof Schütt authored at least 24 papers between 2012 and 2023.

Collaborative distances:

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

2023

Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation.

Tuan Le, Julian Cremer, Frank Noé, Djork-Arné Clevert, Kristof Schütt

CoRR, 2023

2022

Higher-Order Explanations of Graph Neural Networks via Relevant Walks.

Thomas Schnake, Oliver Eberle, Jonas Lederer, Shinichi Nakajima, Kristof T. Schütt, Klaus-Robert Müller, Grégoire Montavon

IEEE Trans. Pattern Anal. Mach. Intell., 2022

Automatic Identification of Chemical Moieties.

Jonas Lederer, Michael Gastegger, Kristof T. Schütt, Michael Kampffmeyer, Klaus-Robert Müller, Oliver T. Unke

CoRR, 2022

2021

Inverse design of 3d molecular structures with conditional generative neural networks.

Niklas W. A. Gebauer, Michael Gastegger, Stefaan S. P. Hessmann, Klaus-Robert Müller, Kristof T. Schütt

CoRR, 2021

SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects.

Oliver T. Unke, Stefan Chmiela, Michael Gastegger, Kristof T. Schütt, Huziel E. Sauceda, Klaus-Robert Müller

CoRR, 2021

Equivariant message passing for the prediction of tensorial properties and molecular spectra.

Kristof Schütt, Oliver T. Unke, Michael Gastegger

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

XAI for Graphs: Explaining Graph Neural Network Predictions by Identifying Relevant Walks.

Thomas Schnake, Oliver Eberle, Jonas Lederer, Shinichi Nakajima, Kristof T. Schütt, Klaus-Robert Müller, Grégoire Montavon

CoRR, 2020

Autonomous robotic nanofabrication with reinforcement learning.

Philipp Leinen, Malte Esders, Kristof T. Schütt, Christian Wagner, Klaus-Robert Müller, F. Stefan Tautz

CoRR, 2020

2019

Quantum-Chemical Insights from Interpretable Atomistic Neural Networks.

Kristof T. Schütt, Michael Gastegger, Alexandre Tkatchenko, Klaus-Robert Müller

Proceedings of the Explainable AI: Interpreting, 2019

The (Un)reliability of Saliency Methods.

Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof T. Schütt, Sven Dähne, Dumitru Erhan, Been Kim

Proceedings of the Explainable AI: Interpreting, 2019

iNNvestigate Neural Networks!

Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, Pieter-Jan Kindermans

J. Mach. Learn. Res., 2019

Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules.

Niklas W. A. Gebauer, Michael Gastegger, Kristof Schütt

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

2018

Learning Representations of Atomistic Systems with Deep Neural Networks.

Kristof Schütt

Proceedings of the Ausgezeichnete Informatikdissertationen 2018., 2018

Learning representations of atomistic systems with deep neural networks (Lernen von Repräsentationen für atomistische Systeme mit tiefen neuronalen Netzen)

Kristof Schütt

PhD thesis, 2018

Learning representations of molecules and materials with atomistic neural networks.

Kristof T. Schütt, Alexandre Tkatchenko, Klaus-Robert Müller

CoRR, 2018

Generating equilibrium molecules with deep neural networks.

Niklas W. A. Gebauer, Michael Gastegger, Kristof T. Schütt

CoRR, 2018

Quantum-chemical insights from interpretable atomistic neural networks.

Kristof T. Schütt, Michael Gastegger, Alexandre Tkatchenko, Klaus-Robert Müller

CoRR, 2018

Learning how to explain neural networks: PatternNet and PatternAttribution.

Pieter-Jan Kindermans, Kristof T. Schütt, Maximilian Alber, Klaus-Robert Müller, Dumitru Erhan, Been Kim, Sven Dähne

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

The (Un)reliability of saliency methods.

Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof T. Schütt, Sven Dähne, Dumitru Erhan, Been Kim

CoRR, 2017

PatternNet and PatternLRP - Improving the interpretability of neural networks.

Pieter-Jan Kindermans, Kristof T. Schütt, Maximilian Alber, Klaus-Robert Müller, Sven Dähne

CoRR, 2017

SchNet: A continuous-filter convolutional neural network for modeling quantum interactions.

Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, Klaus-Robert Müller

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

An Empirical Study on The Properties of Random Bases for Kernel Methods.

Maximilian Alber, Pieter-Jan Kindermans, Kristof Schütt, Klaus-Robert Müller, Fei Sha

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Investigating the influence of noise and distractors on the interpretation of neural networks.

Pieter-Jan Kindermans, Kristof Schütt, Klaus-Robert Müller, Sven Dähne

CoRR, 2016

2012

Early detection of malicious behavior in JavaScript code.

Kristof Schütt, Marius Kloft, Alexander Bikadorov, Konrad Rieck

Proceedings of the 5th ACM Workshop on Security and Artificial Intelligence, 2012