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Saliency strikes back: How filtering out high frequencies improves white-box explanations
Attribution methods correspond to a class of explainability methods (XAI) that aim to assess how individual inputs contribute to a model's decision-making process.
Confident Object Detection via Conformal Prediction and Conformal Risk Control: an Application to Railway Signaling
Deploying deep learning models in real-world certified systems requires the ability to provide confidence estimates that accurately reflect their uncertainty.
Towards Understanding the Mechanism of Contrastive Learning via Similarity Structure: A Theoretical Analysis
We first prove that the formulation characterizes the structure of representations learned with the kernel-based contrastive learning framework.
Conformal Prediction for Trustworthy Detection of Railway Signals
We present an application of conformal prediction, a form of uncertainty quantification with guarantees, to the detection of railway signals.
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