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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 38613870 of 9051 papers

TitleStatusHype
Galaxy Image Simulation Using Progressive GANs0
Compositional Zero-Shot Learning via Fine-Grained Dense Feature Composition0
Compositional Operators in Distributional Semantics0
Explicit Diversity Conditions for Effective Question Answer Generation with Large Language Models0
Explicit and Implicit Pattern Relation Analysis for Discovering Actionable Negative Sequences0
GameVibe: A Multimodal Affective Game Corpus0
Gamification Platform for Collecting Task-oriented Dialogue Data0
GAMMT: Generative Ambiguity Modeling Using Multiple Transformers0
GAN based ball screw drive picture database enlargement for failure classification0
Compositional Fine-Grained Low-Shot Learning0
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