“Post-hoc prototype explanations for classification models” Explainable AI traditionally forces a choice between flexible but vague post-hoc methods (like saliency maps) and intuitive but rigid prototype-based architectures. This talk introduces a framework that bridges this gap: post-hoc prototype explanations. We […]
“Post-hoc prototype explanations for classification models” Explainable AI traditionally forces a choice between flexible but vague post-hoc methods (like saliency maps) and intuitive but rigid prototype-based architectures. This talk introduces a framework that bridges this gap: post-hoc prototype explanations. We […]
“Explainability of Vision-Language Models” In this talk, I will introduce the methodology for explaining vision-language models and discuss the key challenges associated with this research domain. The presentation will highlight our recent scientific results concerning the FIxLIP algorithm published at […]
“Projected Compression: Trainable Projections for Efficient Transformer Compression” Large language models have steadily increased in size to achieve improved performance; however, this growth has also led to greater inference time and computational demands. Consequently, there is rising interest in model […]
