Compositional robustness testing for vision models
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Updated
May 28, 2026 - Python
Compositional robustness testing for vision models
Is your RAG understanding your data - or guessing?
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A robust, translation invariant architecture for generative image watermarking with provable resistance to cross-image codebook subtraction
Parkinson's disease classification from PPMI voice and clinical features with SHAP explainability, noise-robustness testing, and UPDRS progression modeling.
Machine learning pipeline for kidney stone risk prediction, featuring calibrated models, interpretability (Permutation Importance + PDPs), and a clean modular architecture for clinical decision support.
A reproducible edge-case audit of Merlin CT image-text behavior.
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