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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 3441–3450 of 9051 papers

TitleStatusHype
A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes—0
Exploiting Spatial-temporal Correlations for Video Anomaly Detection—0
Exploiting Style Transfer-based Task Augmentation for Cross-Domain Few-Shot Learning—0
Exploiting Web Images for Weakly Supervised Object Detection—0
Explorable Tone Mapping Operators—0
Exploration and Exploitation in Symbolic Regression using Quality-Diversity and Evolutionary Strategies Algorithms—0
Findings of the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion—0
Explorations in an English Poetry Corpus: A Neurocognitive Poetics Perspective—0
Exploratory Data Analysis of Urdu Poetry—0
Diversified Sampling Improves Scaling LLM inference—0
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