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"What is Relevant in a Text Document?": An Interpretable Machine Learning Approach
Leila Arras, Franziska Horn, Grégoire Montavon +2
Text documents can be described by a number of abstract concepts such as semantic category, writing style, or sentiment. Machine learning (ML) models have been trained to automatic…
Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
Sebastian Bosse, Dominique Maniry, Klaus-Robert Müller +2
We present a deep neural network-based approach to image quality assessment (IQA). The network is trained end-to-end and comprises ten convolutional layers and five pooling layers…
Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation
Wojciech Samek, Grégoire Montavon, Alexander Binder +2
Complex nonlinear models such as deep neural network (DNNs) have become an important tool for image classification, speech recognition, natural language processing, and many other…
Feature Importance Measure for Non-linear Learning Algorithms
Marina M. -C. Vidovic, Nico Görnitz, Klaus-Robert Müller +1
Complex problems may require sophisticated, non-linear learning methods such as kernel machines or deep neural networks to achieve state of the art prediction accuracies. However,…
Machine Learning of Accurate Energy-Conserving Molecular Force Fields
Stefan Chmiela, Alexandre Tkatchenko, Huziel E. Sauceda +3
Using conservation of energy - a fundamental property of closed classical and quantum mechanical systems - we develop an efficient gradient-domain machine learning (GDML) approach…
Quantum-Chemical Insights from Deep Tensor Neural Networks
Kristof T. Schütt, Farhad Arbabzadah, Stefan Chmiela +2
Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text, and image search, speech recognition, as well as bioinformatics. Can machine learn…