13 citations · 44 across the 12 of their papers we have counts for
4 papers · 1 filter
Prototype-Based Learning for Healthcare: A Demonstration of Interpretable AI
Ashish Rana, Ammar Shaker, Sascha Saralajew +6
Despite recent advances in machine learning and explainable AI, a gap remains in personalized preventive healthcare: predictions, interventions, and recommendations should be both…
A Robust Prototype-Based Network with Interpretable RBF Classifier Foundations
Sascha Saralajew, Ashish Rana, Thomas Villmann +1
Prototype-based classification learning methods are known to be inherently interpretable. However, this paradigm suffers from major limitations compared to deep models, such as low…
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…