177 citations · 519 across the 32 of their papers we have counts for
3 papers · 1 filter
End-to-end Learning of Deterministic Decision Trees
Thomas Hehn, Fred A. Hamprecht
Conventional decision trees have a number of favorable properties, including interpretability, a small computational footprint and the ability to learn from little training data. H…
Learning Steerable Filters for Rotation Equivariant CNNs
Maurice Weiler, Fred A. Hamprecht, Martin Storath
In many machine learning tasks it is desirable that a model's prediction transforms in an equivariant way under transformations of its input. Convolutional neural networks (CNNs) i…
Learned Watershed: End-to-End Learning of Seeded Segmentation
Steffen Wolf, Lukas Schott, Ullrich Köthe +1
Learned boundary maps are known to outperform hand- crafted ones as a basis for the watershed algorithm. We show, for the first time, how to train watershed computation jointly wit…