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Found 5 papers, 4 papers with code
How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control
Score-based generative modeling, informally referred to as diffusion models, continue to grow in popularity across several important domains and tasks.
Weakly Supervised Learning Significantly Reduces the Number of Labels Required for Intracranial Hemorrhage Detection on Head CT
We find that strong supervision (i. e., learning with local image-level annotations) and weak supervision (i. e., learning with only global examination-level labels) achieve comparable performance in examination-level hemorrhage detection (the task of selecting the images in an examination that show signs of hemorrhage) as well as in image-level hemorrhage detection (highlighting those signs within the selected images).
SHAP-XRT: The Shapley Value Meets Conditional Independence Testing
As a result, we further our understanding of Shapley-based explanation methods from a novel perspective and characterize the conditions under which one can make statistically valid claims about feature importance via the Shapley value.
Fast Hierarchical Games for Image Explanations
As modern complex neural networks keep breaking records and solving harder problems, their predictions also become less and less intelligible.
Fitting Splines to Axonal Arbors Quantifies Relationship between Branch Order and Geometry
Our representation makes it possible to compute these parameters from neuron traces in closed form.
