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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 16911700 of 9051 papers

TitleStatusHype
Information-Seeking Decision Strategies Mitigate Risk in Dynamic, Uncertain EnvironmentsCode0
A Systematic Characterization of Sampling Algorithms for Open-ended Language GenerationCode0
Information-Theoretic Active Learning for Content-Based Image RetrievalCode0
Synthetic Hyperspectral Array Video Database with Applications to Cross-Spectral Reconstruction and Hyperspectral Video CodingCode0
In-Context Example Selection via Similarity Search Improves Low-Resource Machine TranslationCode0
INGB: Informed Nonlinear Granular Ball Oversampling Framework for Noisy Imbalanced ClassificationCode0
Insect Identification in the Wild: The AMI DatasetCode0
Influence Maximization in Hypergraphs using Multi-Objective Evolutionary AlgorithmsCode0
Asymptotic theory of in-context learning by linear attentionCode0
Inference of cell dynamics on perturbation data using adjoint sensitivityCode0
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