2.8k citations · 13.3k across the 90 of their papers we have counts for
14 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 The Invisible: A Hybrid Deep Learning-Shearlet Framework for Limited Angle Computed Tomography
T. A. Bubba, G. Kutyniok, M. Lassas +4
The high complexity of various inverse problems poses a significant challenge to model-based reconstruction schemes, which in such situations often reach their limits. At the same…
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…
Enhanced Machine Learning Techniques for Early HARQ Feedback Prediction in 5G
Nils Strodthoff, Barış Göktepe, Thomas Schierl +2
We investigate Early Hybrid Automatic Repeat reQuest (E-HARQ) feedback schemes enhanced by machine learning techniques as a path towards ultra-reliable and low-latency communicatio…