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2020 – today
- 2024
[c8]Julian Cremer, Tuan Le, Djork-Arné Clevert, Kristof T. Schütt:
Latent-Conditioned Equivariant Diffusion for Structure-Based De Novo Ligand Generation. AIDD@ICANN 2024: 36-46- [c7]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 - [i16]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 abs/2405.14925 (2024) - 2023
- [i15]Tuan Le, Julian Cremer, Frank Noé, Djork-Arné Clevert, Kristof Schütt:
Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation. CoRR abs/2309.17296 (2023) - 2022
- [j2]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): 7581-7596 (2022) - [i14]Jonas Lederer, Michael Gastegger, Kristof T. Schütt, Michael Kampffmeyer, Klaus-Robert Müller, Oliver T. Unke:
Automatic Identification of Chemical Moieties. CoRR abs/2203.16205 (2022) - 2021
- [c6]Kristof Schütt, Oliver T. Unke, Michael Gastegger:
Equivariant message passing for the prediction of tensorial properties and molecular spectra. ICML 2021: 9377-9388 - [i13]Kristof T. Schütt, Oliver T. Unke, Michael Gastegger:
Equivariant message passing for the prediction of tensorial properties and molecular spectra. CoRR abs/2102.03150 (2021) - [i12]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 abs/2105.00304 (2021) - [i11]Niklas W. A. Gebauer, Michael Gastegger, Stefaan Simon Pierre Hessmann, Klaus-Robert Müller, Kristof T. Schütt:
Inverse design of 3d molecular structures with conditional generative neural networks. CoRR abs/2109.04824 (2021) - 2020
- [i10]Philipp Leinen, Malte Esders, Kristof T. Schütt, Christian Wagner, Klaus-Robert Müller, F. Stefan Tautz:
Autonomous robotic nanofabrication with reinforcement learning. CoRR abs/2002.11952 (2020) - [i9]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 abs/2006.03589 (2020)
2010 – 2019
- 2019
- [j1]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: 93:1-93:8 (2019) - [c5]Niklas W. A. Gebauer, Michael Gastegger, Kristof Schütt:
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules. NeurIPS 2019: 7564-7576 - [p3]Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof T. Schütt, Sven Dähne, Dumitru Erhan, Been Kim:
The (Un)reliability of Saliency Methods. Explainable AI 2019: 267-280 - [p2]Kristof T. Schütt, Michael Gastegger, Alexandre Tkatchenko, Klaus-Robert Müller:
Quantum-Chemical Insights from Interpretable Atomistic Neural Networks. Explainable AI 2019: 311-330 - [i8]Niklas W. A. Gebauer, Michael Gastegger, Kristof T. Schütt:
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules. CoRR abs/1906.00957 (2019) - 2018
- [b1]Kristof Schütt:
Learning representations of atomistic systems with deep neural networks (Lernen von Repräsentationen für atomistische Systeme mit tiefen neuronalen Netzen). TU Berlin, Germany, 2018 - [c4]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 - [p1]Kristof Schütt:
Learning Representations of Atomistic Systems with Deep Neural Networks. Ausgezeichnete Informatikdissertationen 2018: 231-240 - [i7]Kristof T. Schütt, Michael Gastegger, Alexandre Tkatchenko, Klaus-Robert Müller:
Quantum-chemical insights from interpretable atomistic neural networks. CoRR abs/1806.10349 (2018) - [i6]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 abs/1808.04260 (2018) - [i5]Niklas W. A. Gebauer, Michael Gastegger, Kristof T. Schütt:
Generating equilibrium molecules with deep neural networks. CoRR abs/1810.11347 (2018) - [i4]Kristof T. Schütt, Alexandre Tkatchenko, Klaus-Robert Müller:
Learning representations of molecules and materials with atomistic neural networks. CoRR abs/1812.04690 (2018) - 2017
- [c3]Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, Klaus-Robert Müller:
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions. NIPS 2017: 991-1001 - [c2]Maximilian Alber, Pieter-Jan Kindermans, Kristof Schütt, Klaus-Robert Müller, Fei Sha:
An Empirical Study on The Properties of Random Bases for Kernel Methods. NIPS 2017: 2763-2774 - [i3]Pieter-Jan Kindermans, Kristof T. Schütt, Maximilian Alber, Klaus-Robert Müller, Sven Dähne:
PatternNet and PatternLRP - Improving the interpretability of neural networks. CoRR abs/1705.05598 (2017) - [i2]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 abs/1711.00867 (2017) - 2016
- [i1]Pieter-Jan Kindermans, Kristof Schütt, Klaus-Robert Müller, Sven Dähne:
Investigating the influence of noise and distractors on the interpretation of neural networks. CoRR abs/1611.07270 (2016) - 2012
- [c1]Kristof Schütt, Marius Kloft, Alexander Bikadorov, Konrad Rieck:
Early detection of malicious behavior in JavaScript code. AISec 2012: 15-24
Coauthor Index
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