Search Results for author:
Found 6 papers, 1 papers with code
Conformal Predictions for Probabilistically Robust Scalable Machine Learning Classification
Conformal predictions make it possible to define reliable and robust learning algorithms.
Probabilistic Safety Regions Via Finite Families of Scalable Classifiers
The notion of scalable classifiers is then exploited to link the tuning of machine learning with error control.
CONFIDERAI: a novel CONFormal Interpretable-by-Design score function for Explainable and Reliable Artificial Intelligence
Everyday life is increasingly influenced by artificial intelligence, and there is no question that machine learning algorithms must be designed to be reliable and trustworthy for everyone.
Rule-based Out-Of-Distribution Detection
Out-of-distribution detection is one of the most critical issue in the deployment of machine learning.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Approximating Optimal Estimation of Time Offset Synchronization with Temperature Variations
The paper addresses the problem of time offset synchronization in the presence of temperature variations, which lead to a non-Gaussian environment.
A neural approach to synchronization in wireless networks with heterogeneous sources of noise
Examples are temperature variations and delay asymmetry.
