2.8k citations · 13.3k across the 90 of their papers we have counts for
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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…
On the Explanation of Machine Learning Predictions in Clinical Gait Analysis
Djordje Slijepcevic, Fabian Horst, Sebastian Lapuschkin +6
Machine learning (ML) is increasingly used to support decision-making in the healthcare sector. While ML approaches provide promising results with regard to their classification pe…
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 Best Practice in Explaining Neural Network Decisions with LRP
Maximilian Kohlbrenner, Alexander Bauer, Shinichi Nakajima +3
Within the last decade, neural network based predictors have demonstrated impressive - and at times super-human - capabilities. This performance is often paid for with an intranspa…