by instadatahelp | Sep 6, 2023 | AI Blogs
Fairlearn: Assessing and Improving Fairness of AI Systems Hilde Weerts, Miroslav Dudík, Richard Edgar, Adrin Jalali, Roman Lutz, Michael Madaio; 24(257):1−8, 2023. Abstract Fairlearn is a project aimed at assisting practitioners in evaluating and enhancing the...
by instadatahelp | Sep 6, 2023 | AI Blogs
arXivLabs is a platform that enables collaborators to develop and share new features for arXiv directly on our website. Both individuals and organizations that work with arXivLabs have fully embraced and accepted our values of openness, community, excellence, and user...
by instadatahelp | Sep 6, 2023 | AI Blogs
Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search Benjamin Moseley, Joshua R. Wang; 24(1):1−36, 2023. Abstract This paper aims to provide an analytical framework for understanding hierarchical clustering, a widely...
by instadatahelp | Sep 6, 2023 | AI Blogs
arXivLabs is a platform that enables collaborators to create and share new features for arXiv directly on our website. Both individuals and organizations that work with arXivLabs share our core values of openness, community, excellence, and user data privacy. We are...
by instadatahelp | Sep 6, 2023 | AI Blogs
Examining the Environmental Impact of Federated Learning Xinchi Qiu, Titouan Parcollet, Javier Fernandez-Marques, Pedro P. B. Gusmao, Yan Gao, Daniel J. Beutel, Taner Topal, Akhil Mathur, Nicholas D. Lane; 24(129):1−23, 2023. Abstract Although deep learning-based...
by instadatahelp | Sep 6, 2023 | AI Blogs
[Submitted on 29 Aug 2023] Click here to download a PDF of the paper titled “Modified Lagrangian Formulation of Gear Tooth Crack Analysis using Combined Approach of Variable Mode Decomposition (VMD) and Time Synchronous Averaging (TSA)” by Subrata...
