28 citations · 50 across the 8 of their papers we have counts for
6 papers · 1 filter
Efficient Neural Network Training via Subset Pretraining
Jan Spörer, Bernhard Bermeitinger, Tomas Hrycej +2
In training neural networks, it is common practice to use partial gradients computed over batches, mostly very small subsets of the training set. This approach is motivated by the…
Reducing the Transformer Architecture to a Minimum
Bernhard Bermeitinger, Tomas Hrycej, Massimo Pavone +2
Transformers are a widespread and successful model architecture, particularly in Natural Language Processing (NLP) and Computer Vision (CV). The essential innovation of this archit…
Make Deep Networks Shallow Again
Bernhard Bermeitinger, Tomas Hrycej, Siegfried Handschuh
Deep neural networks have a good success record and are thus viewed as the best architecture choice for complex applications. Their main shortcoming has been, for a long time, the…
Training Neural Networks in Single vs Double Precision
Tomas Hrycej, Bernhard Bermeitinger, Siegfried Handschuh
The commitment to single-precision floating-point arithmetic is widespread in the deep learning community. To evaluate whether this commitment is justified, the influence of comput…
Representational Capacity of Deep Neural Networks -- A Computing Study
Bernhard Bermeitinger, Tomas Hrycej, Siegfried Handschuh
There is some theoretical evidence that deep neural networks with multiple hidden layers have a potential for more efficient representation of multidimensional mappings than shallo…
Singular Value Decomposition and Neural Networks
Bernhard Bermeitinger, Tomas Hrycej, Siegfried Handschuh
Singular Value Decomposition (SVD) constitutes a bridge between the linear algebra concepts and multi-layer neural networks---it is their linear analogy. Besides of this insight, i…