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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 951960 of 9051 papers

TitleStatusHype
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
Contrastive Syn-to-Real GeneralizationCode1
Controllable Multi-Interest Framework for RecommendationCode1
Attributed Graph Clustering with Dual Redundancy ReductionCode1
A Case for Rejection in Low Resource ML DeploymentCode1
Attribute Group Editing for Reliable Few-shot Image GenerationCode1
Merging and Splitting Diffusion Paths for Semantically Coherent PanoramasCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
Continual Object Detection via Prototypical Task Correlation Guided Gating MechanismCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
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