2.8k citations · 19.2k across the 71 of their papers we have counts for
4 papers · 2 filters
Understanding and Comparing Deep Neural Networks for Age and Gender Classification
Sebastian Lapuschkin, Alexander Binder, Klaus-Robert Müller +1
Recently, deep neural networks have demonstrated excellent performances in recognizing the age and gender on human face images. However, these models were applied in a black-box ma…
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof T. Schütt, Pieter-Jan Kindermans, Huziel E. Sauceda +3
Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical spa…
Learning how to explain neural networks: PatternNet and PatternAttribution
Pieter-Jan Kindermans, Kristof T. Schütt, Maximilian Alber +4
DeConvNet, Guided BackProp, LRP, were invented to better understand deep neural networks. We show that these methods do not produce the theoretically correct explanation for a line…
Predicting Pairwise Relations with Neural Similarity Encoders
Franziska Horn, Klaus-Robert Müller
Matrix factorization is at the heart of many machine learning algorithms, for example, dimensionality reduction (e.g. kernel PCA) or recommender systems relying on collaborative fi…