2.8k citations · 19.2k across the 71 of their papers we have counts for
19 papers · 1 filter
Finding and Removing Clever Hans: Using Explanation Methods to Debug and Improve Deep Models
Christopher J. Anders, Leander Weber, David Neumann +3
Contemporary learning models for computer vision are typically trained on very large (benchmark) datasets with millions of samples. These may, however, contain biases, artifacts, o…
Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning
Seul-Ki Yeom, Philipp Seegerer, Sebastian Lapuschkin +4
The success of convolutional neural networks (CNNs) in various applications is accompanied by a significant increase in computation and parameter storage costs. Recent efforts to r…
Machine learning for molecular simulation
Frank Noé, Alexandre Tkatchenko, Klaus-Robert Müller +1
Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for a machine learning re…
Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints
Felix Sattler, Klaus-Robert Müller, Wojciech Samek
Federated Learning (FL) is currently the most widely adopted framework for collaborative training of (deep) machine learning models under privacy constraints. Albeit it's popularit…
Asymptotically unbiased estimation of physical observables with neural samplers
Kim A. Nicoli, Shinichi Nakajima, Nils Strodthoff +3
We propose a general framework for the estimation of observables with generative neural samplers focusing on modern deep generative neural networks that provide an exact sampling p…
Towards Explainable Artificial Intelligence
Wojciech Samek, Klaus-Robert Müller
In recent years, machine learning (ML) has become a key enabling technology for the sciences and industry. Especially through improvements in methodology, the availability of large…