9 citations · 14 across the 3 of their papers we have counts for
3 papers
Utilizing Expert Features for Contrastive Learning of Time-Series Representations
Manuel Nonnenmacher, Lukas Oldenburg, Ingo Steinwart +1
We present an approach that incorporates expert knowledge for time-series representation learning. Our method employs expert features to replace the commonly used data transformati…
SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning
Manuel Nonnenmacher, Thomas Pfeil, Ingo Steinwart +1
Pruning neural networks reduces inference time and memory costs. On standard hardware, these benefits will be especially prominent if coarse-grained structures, like feature maps,…
Which Minimizer Does My Neural Network Converge To?
Manuel Nonnenmacher, David Reeb, Ingo Steinwart
The loss surface of an overparameterized neural network (NN) possesses many global minima of zero training error. We explain how common variants of the standard NN training procedu…