10 citations · 10 across the 6 of their papers we have counts for
6 papers
Conveyance: A Versatile Framework for Learning in Structured Class Spaces
Yasser Taha, Grégoire Montavon, Nils Körber
While machine learning (ML) architectures have evolved rapidly to account for complex data, loss functions like cross-entropy remain mostly structure-agnostic in many real-world ap…
Evaluating quality in synthetic data generation for large tabular health datasets
Jean-Baptiste Escudié, Benjamin Barnes, Stefan Meisegeier +3
There is no consensus in the field of synthetic data on concise metrics for quality evaluations or benchmarks on large health datasets, such as historical epidemiological data. Thi…
How to Measure the Intelligence of Large Language Models?
Nils Körber, Silvan Wehrli, Christopher Irrgang
With the release of ChatGPT and other large language models (LLMs) the discussion about the intelligence, possibilities, and risks, of current and future models have seen large att…
GANetic Loss for Generative Adversarial Networks with a Focus on Medical Applications
Shakhnaz Akhmedova, Nils Körber
Generative adversarial networks (GANs) are machine learning models that are used to estimate the underlying statistical structure of a given dataset and as a result can be used for…
Next Generation Loss Function for Image Classification
Shakhnaz Akhmedova, Nils Körber
Neural networks are trained by minimizing a loss function that defines the discrepancy between the predicted model output and the target value. The selection of the loss function i…
Parameter-Free Average Attention Improves Convolutional Neural Network Performance (Almost) Free of Charge
Nils Körber
Visual perception is driven by the focus on relevant aspects in the surrounding world. To transfer this observation to the digital information processing of computers, attention me…