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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 671680 of 9051 papers

TitleStatusHype
SimMMDG: A Simple and Effective Framework for Multi-modal Domain GeneralizationCode1
Controllable Group Choreography using Contrastive DiffusionCode1
Chain-of-Choice Hierarchical Policy Learning for Conversational RecommendationCode1
TarGEN: Targeted Data Generation with Large Language ModelsCode1
Semantic Generative Augmentations for Few-Shot CountingCode1
Generative Fractional Diffusion ModelsCode1
AlpaCare:Instruction-tuned Large Language Models for Medical ApplicationCode1
Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection BiasCode1
Diversify Question Generation with Retrieval-Augmented Style TransferCode1
Invariant Feature Regularization for Fair Face RecognitionCode1
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