40 citations · 74 across the 18 of their papers we have counts for
10 papers · 1 filter
Uncertainty Quantification via Stable Distribution Propagation
Felix Petersen, Aashwin Mishra, Hilde Kuehne +3
We propose a new approach for propagating stable probability distributions through neural networks. Our method is based on local linearization, which we show to be an optimal appro…
ISAAC Newton: Input-based Approximate Curvature for Newton's Method
Felix Petersen, Tobias Sutter, Christian Borgelt +4
We present ISAAC (Input-baSed ApproximAte Curvature), a novel method that conditions the gradient using selected second-order information and has an asymptotically vanishing comput…
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…
Learning with Differentiable Algorithms
Felix Petersen
Classic algorithms and machine learning systems like neural networks are both abundant in everyday life. While classic computer science algorithms are suitable for precise executio…
Differentiable Top-k Classification Learning
Felix Petersen, Hilde Kuehne, Christian Borgelt +1
The top-k classification accuracy is one of the core metrics in machine learning. Here, k is conventionally a positive integer, such as 1 or 5, leading to top-1 or top-5 training o…
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…
Towards Formula Translation using Recursive Neural Networks
Felix Petersen, Moritz Schubotz, Bela Gipp
While it has become common to perform automated translations on natural language, performing translations between different representations of mathematical formulae has thus far no…