3 papers
cs.LG2025
ScoresActivation: A New Activation Function for Model Agnostic Global Explainability by Design
Emanuel Covaci, Fabian Galis, Radu Balan +2
Understanding the decision of large deep learning models is a critical challenge for building transparent and trustworthy systems. Although the current post hoc explanation methods…
cs.NE2025
Benchmarking that Matters: Rethinking Benchmarking for Practical Impact
Anna V. Kononova, Niki van Stein, Olaf Mersmann +14
Benchmarking has driven scientific progress in Evolutionary Computation, yet current practices fall short of real-world needs. Widely used synthetic suites such as BBOB and CEC iso…
cs.CL2025
TT-XAI: Trustworthy Clinical Text Explanations via Keyword Distillation and LLM Reasoning
Kristian Miok, Blaz Å krlj, Daniela Zaharie +1
Clinical language models often struggle to provide trustworthy predictions and explanations when applied to lengthy, unstructured electronic health records (EHRs). This work introd…