Kristof T. Schütt

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 18

Top Topics

Importance Sampling

Pareto Frontier

Top Venues

J. Mach. Learn. Res.

IEEE Trans. Pattern Anal. Mach. Intell.

Publications

Julian Cremer, Tuan Le, Frank Noé, Djork-Arné Clevert, Kristof T. Schütt
PILOT: Equivariant diffusion for pocket conditioned de novo ligand generation with multi-objective guidance via importance sampling. CoRR (2024)
Tuan Le, Julian Cremer, Frank Noé, Djork-Arné Clevert, Kristof T. Schütt
Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation. ICLR (2024)
Jonas Lederer, Michael Gastegger, Kristof T. Schütt, Michael Kampffmeyer, Klaus-Robert Müller, Oliver T. Unke
Automatic Identification of Chemical Moieties. CoRR (2022)
Thomas Schnake, Oliver Eberle, Jonas Lederer, Shinichi Nakajima, Kristof T. Schütt, Klaus-Robert Müller, Grégoire Montavon
Higher-Order Explanations of Graph Neural Networks via Relevant Walks. IEEE Trans. Pattern Anal. Mach. Intell. 44 (11) (2022)
Oliver T. Unke, Stefan Chmiela, Michael Gastegger, Kristof T. Schütt, Huziel E. Sauceda, Klaus-Robert Müller
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects. CoRR (2021)
Kristof T. Schütt, Oliver T. Unke, Michael Gastegger
Equivariant message passing for the prediction of tensorial properties and molecular spectra. CoRR (2021)
Niklas W. A. Gebauer, Michael Gastegger, Stefaan S. P. Hessmann, Klaus-Robert Müller, Kristof T. Schütt
Inverse design of 3d molecular structures with conditional generative neural networks. CoRR (2021)
Thomas Schnake, Oliver Eberle, Jonas Lederer, Shinichi Nakajima, Kristof T. Schütt, Klaus-Robert Müller, Grégoire Montavon
XAI for Graphs: Explaining Graph Neural Network Predictions by Identifying Relevant Walks. CoRR (2020)
Philipp Leinen, Malte Esders, Kristof T. Schütt, Christian Wagner, Klaus-Robert Müller, F. Stefan Tautz
Autonomous robotic nanofabrication with reinforcement learning. CoRR (2020)
Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, Pieter-Jan Kindermans
iNNvestigate Neural Networks! J. Mach. Learn. Res. 20 (2019)
Niklas W. A. Gebauer, Michael Gastegger, Kristof T. Schütt
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules. CoRR (2019)
Kristof T. Schütt, Michael Gastegger, Alexandre Tkatchenko, Klaus-Robert Müller
Quantum-chemical insights from interpretable atomistic neural networks. CoRR (2018)
Kristof T. Schütt, Alexandre Tkatchenko, Klaus-Robert Müller
Learning representations of molecules and materials with atomistic neural networks. CoRR (2018)
Pieter-Jan Kindermans, Kristof T. Schütt, Maximilian Alber, Klaus-Robert Müller, Dumitru Erhan, Been Kim, Sven Dähne
Learning how to explain neural networks: PatternNet and PatternAttribution. ICLR (Poster) (2018)
Niklas W. A. Gebauer, Michael Gastegger, Kristof T. Schütt
Generating equilibrium molecules with deep neural networks. CoRR (2018)
Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, Pieter-Jan Kindermans
iNNvestigate neural networks! CoRR (2018)
Pieter-Jan Kindermans, Kristof T. Schütt, Maximilian Alber, Klaus-Robert Müller, Sven Dähne
PatternNet and PatternLRP - Improving the interpretability of neural networks. CoRR (2017)
Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof T. Schütt, Sven Dähne, Dumitru Erhan, Been Kim
The (Un)reliability of saliency methods. CoRR (2017)