2.8k citations · 19.2k across the 71 of their papers we have counts for
15 papers · 1 filter
Entropy-Constrained Training of Deep Neural Networks
Simon Wiedemann, Arturo Marban, Klaus-Robert Müller +1
We propose a general framework for neural network compression that is motivated by the Minimum Description Length (MDL) principle. For that we first derive an expression for the en…
Learning representations of molecules and materials with atomistic neural networks
Kristof T. Schütt, Alexandre Tkatchenko, Klaus-Robert Müller
Deep Learning has been shown to learn efficient representations for structured data such as image, text or audio. In this chapter, we present neural network architectures that are…
Analyzing Neuroimaging Data Through Recurrent Deep Learning Models
Armin W. Thomas, Hauke R. Heekeren, Klaus-Robert Müller +1
The application of deep learning (DL) models to neuroimaging data poses several challenges, due to the high dimensionality, low sample size and complex temporo-spatial dependency s…
SchNetPack: A Deep Learning Toolbox For Atomistic Systems
K. T. Schütt, P. Kessel, M. Gastegger +3
SchNetPack is a toolbox for the development and application of deep neural networks to the prediction of potential energy surfaces and other quantum-chemical properties of molecule…
iNNvestigate neural networks!
Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer +7
In recent years, deep neural networks have revolutionized many application domains of machine learning and are key components of many critical decision or predictive processes. The…
Explaining the Unique Nature of Individual Gait Patterns with Deep Learning
Fabian Horst, Sebastian Lapuschkin, Wojciech Samek +2
Machine learning (ML) techniques such as (deep) artificial neural networks (DNN) are solving very successfully a plethora of tasks and provide new predictive models for complex phy…