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Found 5 papers, 3 papers with code
CAGE: Causality-Aware Shapley Value for Global Explanations
One way to explain AI models is to elucidate the predictive importance of the input features for the AI model in general, also referred to as global explanations.
EduGym: An Environment and Notebook Suite for Reinforcement Learning Education
To solve this issue we introduce EduGym, a set of educational reinforcement learning environments and associated interactive notebooks tailored for education.
Evaluation of GPT-4 for chest X-ray impression generation: A reader study on performance and perception
To generate and evaluate impressions of chest X-rays based on different input modalities (image, text, text and image), a blinded radiological report was written for 25-cases of the publicly available NIH-dataset.
Improving image quality of sparse-view lung tumor CT images with U-Net
Six corresponding sparse-view CT data subsets at varying levels of undersampling were reconstructed from sinograms using filtered backprojection with 16, 32, 64, 128, 256, and 512 views.
A Meta-Reinforcement Learning Algorithm for Causal Discovery
Causal discovery is a major task with the utmost importance for machine learning since causal structures can enable models to go beyond pure correlation-based inference and significantly boost their performance.
