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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 25712580 of 9051 papers

TitleStatusHype
A hybrid transformer and attention based recurrent neural network for robust and interpretable sentiment analysis of tweetsCode1
A Simple Yet Effective Approach for Diversified Session-Based RecommendationCode0
Rationale-based Opinion SummarizationCode0
Advancing the Arabic WordNet: Elevating Content Quality0
Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics0
FairRAG: Fair Human Generation via Fair Retrieval Augmentation0
SubjectDrive: Scaling Generative Data in Autonomous Driving via Subject Control0
GANTASTIC: GAN-based Transfer of Interpretable Directions for Disentangled Image Editing in Text-to-Image Diffusion Models0
Instruction-based Hypergraph Pretraining0
Uncertainty-Aware Deep Video Compression with Ensembles0
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