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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 14811490 of 9051 papers

TitleStatusHype
One2Set: Generating Diverse Keyphrases as a SetCode1
Generating Novel Scene Compositions from Single Images and VideosCode1
Online Continual Learning on a Contaminated Data Stream with Blurry Task BoundariesCode1
Online Damage Recovery for Physical Robots with Hierarchical Quality-DiversityCode1
On Pretraining Data Diversity for Self-Supervised LearningCode1
On the Affinity, Rationality, and Diversity of Hierarchical Topic ModelingCode1
On the Role of Conceptualization in Commonsense Knowledge Graph ConstructionCode1
OpenGCD: Assisting Open World Recognition with Generalized Category DiscoveryCode1
Adaptive Diffusion Terrain Generator for Autonomous Uneven Terrain NavigationCode1
Contrastive Syn-to-Real GeneralizationCode1
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