40 citations · 63 across the 9 of their papers we have counts for
6 papers · 1 filter
Deep Differentiable Logic Gate Networks
Felix Petersen, Christian Borgelt, Hilde Kuehne +1
Recently, research has increasingly focused on developing efficient neural network architectures. In this work, we explore logic gate networks for machine learning tasks by learnin…
Monotonic Differentiable Sorting Networks
Felix Petersen, Christian Borgelt, Hilde Kuehne +1
Differentiable sorting algorithms allow training with sorting and ranking supervision, where only the ordering or ranking of samples is known. Various methods have been proposed to…
Learning with Algorithmic Supervision via Continuous Relaxations
Felix Petersen, Christian Borgelt, Hilde Kuehne +1
The integration of algorithmic components into neural architectures has gained increased attention recently, as it allows training neural networks with new forms of supervision suc…
Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision
Felix Petersen, Christian Borgelt, Hilde Kuehne +1
Sorting and ranking supervision is a method for training neural networks end-to-end based on ordering constraints. That is, the ground truth order of sets of samples is known, whil…
AlgoNet: Smooth Algorithmic Neural Networks
Felix Petersen, Christian Borgelt, Oliver Deussen
Artificial neural networks revolutionized many areas of computer science in recent years since they provide solutions to a number of previously unsolved problems. On the other hand…
Uncertainty-Aware Principal Component Analysis
Jochen Görtler, Thilo Spinner, Dirk Streeb +2
We present a technique to perform dimensionality reduction on data that is subject to uncertainty. Our method is a generalization of traditional principal component analysis (PCA)…