100 citations · 390 across the 21 of their papers we have counts for
4 papers · 1 filter
End-to-End Neural Network Training for Hyperbox-Based Classification
Denis Mayr Lima Martins, Christian Lülf, Fabian Gieseke
Hyperbox-based classification has been seen as a promising technique in which decisions on the data are represented as a series of orthogonal, multidimensional boxes (i.e., hyperbo…
Magnitude and Uncertainty Pruning Criterion for Neural Networks
Vinnie Ko, Stefan Oehmcke, Fabian Gieseke
Neural networks have achieved dramatic improvements in recent years and depict the state-of-the-art methods for many real-world tasks nowadays. One drawback is, however, that many…
Input Selection for Bandwidth-Limited Neural Network Inference
Stefan Oehmcke, Fabian Gieseke
Data are often accommodated on centralized storage servers. This is the case, for instance, in remote sensing and astronomy, where projects produce several petabytes of data every…
Training Big Random Forests with Little Resources
Fabian Gieseke, Christian Igel
Without access to large compute clusters, building random forests on large datasets is still a challenging problem. This is, in particular, the case if fully-grown trees are desire…