50 citations · 161 across the 20 of their papers we have counts for
4 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…
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…
GenDR: A Generalized Differentiable Renderer
Felix Petersen, Bastian Goldluecke, Christian Borgelt +1
In this work, we present and study a generalized family of differentiable renderers. We discuss from scratch which components are necessary for differentiable rendering and formali…
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…