1 citations · 1 across the 6 of their papers we have counts for
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Distributional Loss for Robust Classification
Kathleen Anderson, Thomas Martinetz
This paper proposes a novel loss concept for supervised classification tasks. Rather than enforcing a direct mapping from each input sample to a single assigned label, we define an…
Nearest-Neighbor Density Estimation for Dependency Suppression
Kathleen Anderson, Thomas Martinetz
The ability to remove unwanted dependencies from data is crucial in various domains, including fairness, robust learning, and privacy protection. In this work, we propose an encode…
The Resurrection of the ReLU
Coşku Can Horuz, Geoffrey Kasenbacher, Saya Higuchi +7
Modeling sophisticated activation functions within deep learning architectures has evolved into a distinct research direction. Functions such as GELU, SELU, and SiLU offer smooth g…
Why LLMs Cannot Think and How to Fix It
Marius Jahrens, Thomas Martinetz
This paper elucidates that current state-of-the-art Large Language Models (LLMs) are fundamentally incapable of making decisions or developing "thoughts" within the feature space d…