4 papers
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…
Aligning Generalisation Between Humans and Machines
Filip Ilievski, Barbara Hammer, Frank van Harmelen +22
Recent advances in AI -- including generative approaches -- have resulted in technology that can support humans in scientific discovery and forming decisions, but may also disrupt…
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…
Robust Text Classification: Analyzing Prototype-Based Networks
Zhivar Sourati, Darshan Deshpande, Filip Ilievski +2
Downstream applications often require text classification models to be accurate and robust. While the accuracy of the state-of-the-art Language Models (LMs) approximates human perf…