40 citations · 53 across the 8 of their papers we have counts for
10 papers
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…
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…
Post-processing for Individual Fairness
Felix Petersen, Debarghya Mukherjee, Yuekai Sun +1
Post-processing in algorithmic fairness is a versatile approach for correcting bias in ML systems that are already used in production. The main appeal of post-processing is that it…
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…
Style Agnostic 3D Reconstruction via Adversarial Style Transfer
Felix Petersen, Bastian Goldluecke, Oliver Deussen +1
Reconstructing the 3D geometry of an object from an image is a major challenge in computer vision. Recently introduced differentiable renderers can be leveraged to learn the 3D geo…