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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 22612270 of 9051 papers

TitleStatusHype
Automatically Generating Numerous Context-Driven SFT Data for LLMs across Diverse GranularityCode1
Fair Federated Learning under Domain Skew with Local Consistency and Domain DiversityCode1
Enhancing Feature Diversity Boosts Channel-Adaptive Vision TransformersCode0
Pessimistic Backward Policy for GFlowNetsCode0
Graph Neural PDE Solvers with Conservation and Similarity-EquivarianceCode1
USD: Unsupervised Soft Contrastive Learning for Fault Detection in Multivariate Time SeriesCode1
ART: Automatic Red-teaming for Text-to-Image Models to Protect Benign UsersCode1
Diffusion Bridge Implicit ModelsCode2
Quality-aware Masked Diffusion Transformer for Enhanced Music GenerationCode4
Towards a Probabilistic Fusion Approach for Robust Battery Prognostics0
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