Aug 31, 2023 · This paper focuses on the Explainable Artificial Intelligence topic, aimed to overcome the highlighted critical issues by providing users and practitioners ...
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This practical book shows data scientists, software and site reliability engineers, product managers, and business owners how to run and establish ML reliably.
This study addresses the critical need for reliable and cost-effective MP analysis methods through validation of a semi-automated workflow.
The increasing interest in machine and deep learning applications has highlighted how much powerful is to have notable amounts of data and complex models ...
This practical book shows data scientists, software and site reliability engineers, product managers, and business owners how to run and establish ML reliably.
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The book examines meaningful metrics for diverse types of learning paradigms and applications, unbiased estimation methods, rigorous statistical analysis, fair training sets, and meaningful explainability, all of which are essential to building robust and reliable machine learning products.
Apr 17, 2024 · This work presents a novel and reliable approach to empirically evaluating unlearning algorithms, paving the way for the development of more effective ...
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Oct 6, 2022 · This paper establishes a measure, namely Congruity, to provide information about the reliability of ML/DL model results.
May 10, 2023 · The project aims at designing a machine learning-based model for recognizing sign language to automatically transcribe sign language videos into text.
Jul 14, 2022 · We present a framework for reliable deep learning as a new perspective about a model's abilities; this includes a number of concrete tasks and datasets for ...