Klaus-Robert Müller

research

∙ 07/05/2023

Set Learning for Accurate and Calibrated Models

Model overconfidence and poor calibration are common in machine learning...

0 Lukas Muttenthaler, et al. ∙

research

∙ 04/19/2023

Towards transparent and robust data-driven wind turbine power curve models

Wind turbine power curve models translate ambient conditions into turbin...

0 Simon Letzgus, et al. ∙

research

∙ 04/12/2023

Preemptively Pruning Clever-Hans Strategies in Deep Neural Networks

Explainable AI has become a popular tool for validating machine learning...

0 Lorenz Linhardt, et al. ∙

research

∙ 03/09/2023

Mark My Words: Dangers of Watermarked Images in ImageNet

The utilization of pre-trained networks, especially those trained on Ima...

0 Kirill Bykov, et al. ∙

research

∙ 12/30/2022

Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces

Explainable AI transforms opaque decision strategies of ML models into e...

0 Pattarawat Chormai, et al. ∙

research

∙ 12/24/2022

Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergence

Kernel machines have sustained continuous progress in the field of quant...

0 Stefan Blücher, et al. ∙

research

∙ 11/22/2022

Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations

While the evaluation of explanations is an important step towards trustw...

0 Alexander Binder, et al. ∙

research

∙ 08/25/2022

Algorithmic Differentiation for Automatized Modelling of Machine Learned Force Fields

Reconstructing force fields (FF) from atomistic simulation data is a cha...

20 Niklas Frederik Schmitz, et al. ∙

research

∙ 06/10/2022

Diffeomorphic Counterfactuals with Generative Models

Counterfactuals can explain classification decisions of neural networks ...

15 Ann-Kathrin Dombrowski, et al. ∙

research

∙ 06/09/2022

DORA: Exploring outlier representations in Deep Neural Networks

Deep Neural Networks (DNNs) draw their power from the representations th...

27 Kirill Bykov, et al. ∙

research

∙ 05/28/2022

So3krates – Self-attention for higher-order geometric interactions on arbitrary length-scales

The application of machine learning methods in quantum chemistry has ena...

0 J. Thorben Frank, et al. ∙

research

∙ 05/23/2022

Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images

Traditionally anomaly detection (AD) is treated as an unsupervised probl...

24 Philipp Liznerski, et al. ∙

research

∙ 05/17/2022

Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations

Molecular dynamics (MD) simulations allow atomistic insights into chemic...

11 Oliver T. Unke, et al. ∙

research

∙ 03/30/2022

Automatic Identification of Chemical Moieties

In recent years, the prediction of quantum mechanical observables with m...

18 Jonas Lederer, et al. ∙

research

∙ 02/15/2022

XAI for Transformers: Better Explanations through Conservative Propagation

Transformers have become an important workhorse of machine learning, wit...

59 Ameen Ali, et al. ∙

research

∙ 01/07/2022

Automated Dissipation Control for Turbulence Simulation with Shell Models

The application of machine learning (ML) techniques, especially neural n...

7 Ann-Kathrin Dombrowski, et al. ∙

research

∙ 01/02/2022

Super-resolution in Molecular Dynamics Trajectory Reconstruction with Bi-Directional Neural Networks

Molecular dynamics simulations are a cornerstone in science, allowing to...

7 Ludwig Winkler, et al. ∙

research

∙ 12/21/2021

Toward Explainable AI for Regression Models

In addition to the impressive predictive power of machine learning (ML) ...

83 Simon Letzgus, et al. ∙

research

∙ 11/14/2021

Scrutinizing XAI using linear ground-truth data with suppressor variables

Machine learning (ML) is increasingly often used to inform high-stakes d...

10 Rick Wilming, et al. ∙

research

∙ 11/02/2021

Efficient Hierarchical Bayesian Inference for Spatio-temporal Regression Models in Neuroimaging

Several problems in neuroimaging and beyond require inference on the par...

10 Ali Hashemi, et al. ∙

research

∙ 11/01/2021

Evaluating deep transfer learning for whole-brain cognitive decoding

Research in many fields has shown that transfer learning (TL) is well-su...

13 Armin W. Thomas, et al. ∙

research

∙ 09/10/2021

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

The rational design of molecules with desired properties is a long-stand...

22 Niklas W. A. Gebauer, et al. ∙

research

∙ 08/23/2021

Explaining Bayesian Neural Networks

To make advanced learning machines such as Deep Neural Networks (DNNs) m...

17 Kirill Bykov, et al. ∙

research

∙ 06/25/2021

On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy

There is an increasing number of medical use-cases where classification ...

10 Vignesh Srinivasan, et al. ∙

research

∙ 06/24/2021

Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy

Deep Neural Networks (DNNs) are known to be strong predictors, but their...

20 Christopher J. Anders, et al. ∙

research

∙ 06/09/2021

Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization

Domain shifts in the training data are common in practical applications ...

59 Léo Andéol, et al. ∙

research

∙ 06/08/2021

BIGDML: Towards Exact Machine Learning Force Fields for Materials

Machine-learning force fields (MLFF) should be accurate, computationally...

26 Huziel E. Sauceda, et al. ∙

research

∙ 06/04/2021

SE(3)-equivariant prediction of molecular wavefunctions and electronic densities

Machine learning has enabled the prediction of quantum chemical properti...

12 Oliver T. Unke, et al. ∙

research

∙ 05/25/2021

Optimal Sampling Density for Nonparametric Regression

We propose a novel active learning strategy for regression, which is mod...

14 Danny Panknin, et al. ∙

research

∙ 05/01/2021

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

In recent years, machine-learned force fields (ML-FFs) have gained incre...

10 Oliver T. Unke, et al. ∙

research

∙ 12/18/2020

Towards Robust Explanations for Deep Neural Networks

Explanation methods shed light on the decision process of black-box clas...

27 Ann-Kathrin Dombrowski, et al. ∙

research

∙ 10/28/2020

Machine learning of solvent effects on molecular spectra and reactions

Fast and accurate simulation of complex chemical systems in environments...

41 Michael Gastegger, et al. ∙

research

∙ 10/14/2020

Machine Learning Force Fields

In recent years, the use of Machine Learning (ML) in computational chemi...

0 Oliver T. Unke, et al. ∙

research

∙ 09/24/2020

A Unifying Review of Deep and Shallow Anomaly Detection

Deep learning approaches to anomaly detection have recently improved the...

49 Lukas Ruff, et al. ∙

research

∙ 08/31/2020

Langevin Cooling for Domain Translation

Domain translation is the task of finding correspondence between two dom...

5 Vignesh Srinivasan, et al. ∙

research

∙ 07/20/2020

Fairwashing Explanations with Off-Manifold Detergent

Explanation methods promise to make black-box classifiers more transpare...

0 Christopher J. Anders, et al. ∙

research

∙ 07/03/2020

Explainable Deep One-Class Classification

Deep one-class classification variants for anomaly detection learn a map...

19 Philipp Liznerski, et al. ∙

research

∙ 06/18/2020

The Clever Hans Effect in Anomaly Detection

The 'Clever Hans' effect occurs when the learned model produces correct ...

21 Jacob Kauffmann, et al. ∙

research

∙ 06/16/2020

How Much Can I Trust You? – Quantifying Uncertainties in Explaining Neural Networks

Explainable AI (XAI) aims to provide interpretations for predictions mad...

0 Kirill Bykov, et al. ∙

research

∙ 06/05/2020

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

Graph Neural Networks (GNNs) are a popular approach for predicting graph...

0 Thomas Schnake, et al. ∙

research

∙ 05/30/2020

Rethinking Assumptions in Deep Anomaly Detection

Though anomaly detection (AD) can be viewed as a classification problem ...

51 Lukas Ruff, et al. ∙

research

∙ 05/04/2020

Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach

Gradient-domain machine learning (GDML) is an accurate and efficient app...

62 Jiang Wang, et al. ∙

research

∙ 04/22/2020

Risk Estimation of SARS-CoV-2 Transmission from Bluetooth Low Energy Measurements

Digital contact tracing approaches based on Bluetooth low energy (BLE) h...

13 Felix Sattler, et al. ∙

research

∙ 03/20/2020

Automatic Identification of Types of Alterations in Historical Manuscripts

Alterations in historical manuscripts such as letters represent a promis...

11 David Lassner, et al. ∙

research

∙ 03/17/2020

Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond

With the broader and highly successful usage of machine learning in indu...

7 Wojciech Samek, et al. ∙

research

∙ 03/11/2020

Building and Interpreting Deep Similarity Models

Many learning algorithms such as kernel machines, nearest neighbors, clu...

0 Oliver Eberle, et al. ∙

research

∙ 02/27/2020

Autonomous robotic nanofabrication with reinforcement learning

The ability to handle single molecules as effectively as macroscopic bui...

9 Philipp Leinen, et al. ∙

research

∙ 02/05/2020

Forecasting Industrial Aging Processes with Machine Learning Methods

By accurately predicting industrial aging processes (IAPs), it is possib...

30 Mihail Bogojeski, et al. ∙

research

∙ 12/22/2019

Analyzing ImageNet with Spectral Relevance Analysis: Towards ImageNet un-Hans'ed

Today's machine learning models for computer vision are typically traine...

41 Christopher J. Anders, et al. ∙

research

∙ 12/18/2019

Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning

The success of convolutional neural networks (CNNs) in various applicati...

68 Seul-Ki Yeom, et al. ∙

Klaus-Robert Müller

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