267 citations · 399 across the 9 of their papers we have counts for
3 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…
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…
Compact and Computationally Efficient Representation of Deep Neural Networks
Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek
At the core of any inference procedure in deep neural networks are dot product operations, which are the component that require the highest computational resources. A common approa…