2.8k citations · 13.3k across the 90 of their papers we have counts for
8 papers · 2 filters
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…
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…
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…
Understanding Patch-Based Learning by Explaining Predictions
Christopher Anders, Grégoire Montavon, Wojciech Samek +1
Deep networks are able to learn highly predictive models of video data. Due to video length, a common strategy is to train them on small video snippets. We apply the deep Taylor /…
Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication
Felix Sattler, Simon Wiedemann, Klaus-Robert Müller +1
Currently, progressively larger deep neural networks are trained on ever growing data corpora. As this trend is only going to increase in the future, distributed training schemes a…