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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

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Showing 1611–1620 of 9051 papers

TitleStatusHype
Autobots@LT-EDI-EACL2021: One World, One Family: Hope Speech Detection with BERT Transformer Model—0
AutoAlpha: an Efficient Hierarchical Evolutionary Algorithm for Mining Alpha Factors in Quantitative Investment—0
A User-Centered Investigation of Personal Music Tours—0
Cross-Cutting Political Awareness through Diverse News Recommendations—0
Cross-Dataset Generalization in Deep Learning—0
A Universal Sets-level Optimization Framework for Next Set Recommendation—0
A universally consistent learning rule with a universally monotone error—0
A Universality-Individuality Integration Model for Dialog Act Classification—0
A Universal Density Matrix Functional from Molecular Orbital-Based Machine Learning: Transferability across Organic Molecules—0
A Low Complexity Space-Frequency Multiuser Scheduling Algorithm—0
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