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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 29512960 of 9051 papers

TitleStatusHype
Evolutionary bagging for ensemble learningCode0
Decoding MIE: A Novel Dataset Approach Using Topic Extraction and Affiliation ParsingCode0
Evidence for a multi-level trophic organization of the human gut microbiomeCode0
Evolutionary Generative Adversarial NetworksCode0
A variational selection mechanism for article comment generationCode0
Event Transition Planning for Open-ended Text GenerationCode0
EventDrop: data augmentation for event-based learningCode0
Dynamic Quality-Diversity SearchCode0
Evolution of a Functionally Diverse Swarm via a Novel Decentralised Quality-Diversity AlgorithmCode0
Evaluating Neural Language Models as Cognitive Models of Language AcquisitionCode0
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