149 citations · 261 across the 8 of their papers we have counts for
8 papers
Brilliant Challenges Optimization Problem Submission Contest Final Report
Jan Badura, Artur Laskowski, Maciej Antczak +5
This paper concludes the Brilliant Challenges contest. Participants had to design interesting optimization problems and publish them using the Optil.io platform. It was the first w…
Robustness of Generalized Learning Vector Quantization Models against Adversarial Attacks
Sascha Saralajew, Lars Holdijk, Maike Rees +1
Adversarial attacks and the development of (deep) neural networks robust against them are currently two widely researched topics. The robustness of Learning Vector Quantization (LV…
Prototype-based Neural Network Layers: Incorporating Vector Quantization
Sascha Saralajew, Lars Holdijk, Maike Rees +1
Neural networks currently dominate the machine learning community and they do so for good reasons. Their accuracy on complex tasks such as image classification is unrivaled at the…
Activation Functions for Generalized Learning Vector Quantization - A Performance Comparison
Thomas Villmann, John Ravichandran, Andrea Villmann +2
An appropriate choice of the activation function (like ReLU, sigmoid or swish) plays an important role in the performance of (deep) multilayer perceptrons (MLP) for classification…
Regularization in Relevance Learning Vector Quantization Using l one Norms
Martin Riedel, Marika Kästner, Fabrice Rossi +1
We propose in this contribution a method for l one regularization in prototype based relevance learning vector quantization (LVQ) for sparse relevance profiles. Sparse relevance pr…
Batch and median neural gas
Marie Cottrell, Barbara Hammer, Alexander Hasenfuss +1
Neural Gas (NG) constitutes a very robust clustering algorithm given euclidian data which does not suffer from the problem of local minima like simple vector quantization, or topol…