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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 3781–3790 of 9051 papers

TitleStatusHype
Forecasting high-dimensional dynamics exploiting suboptimal embeddings—0
Forecast with Forecasts: Diversity Matters—0
Genetic Algorithm enhanced by Deep Reinforcement Learning in parent selection mechanism and mutation : Minimizing makespan in permutation flow shop scheduling problems—0
Coronary Heart Disease Diagnosis Based on Improved Ensemble Learning—0
Forgotten Knowledge: Examining the Citational Amnesia in NLP—0
Boosting Diffusion Model for Spectrogram Up-sampling in Text-to-speech: An Empirical Study—0
Formalising lexical and syntactic diversity for data sampling in French—0
Forming Diverse Teams from Sequentially Arriving People—0
Corpus COFLA: A research corpus for the Computational study of Flamenco Music—0
Offline Diversity Maximization Under Imitation Constraints—0
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