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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

Showing 31913200 of 9051 papers

TitleStatusHype
Current State-of-the-Art of Bias Detection and Mitigation in Machine Translation for African and European Languages: a Review0
A Universal Sets-level Optimization Framework for Next Set Recommendation0
Current and future directions in network biology0
A universally consistent learning rule with a universally monotone error0
A Universality-Individuality Integration Model for Dialog Act Classification0
Curiosity creates Diversity in Policy Search0
A Universal Density Matrix Functional from Molecular Orbital-Based Machine Learning: Transferability across Organic Molecules0
A Low Complexity Space-Frequency Multiuser Scheduling Algorithm0
Adapting ELM to Time Series Classification: A Novel Diversified Top-k Shapelets Extraction Method0
Curatr: A Platform for Semantic Analysis and Curation of Historical Literary Texts0
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