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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 691700 of 9051 papers

TitleStatusHype
FuncGenFoil: Airfoil Generation and Editing Model in Function SpaceCode0
Direct Preference Optimization-Enhanced Multi-Guided Diffusion Model for Traffic Scenario Generation0
Expert-Agnostic Learning to Defer0
Enhancing Age-Related Robustness in Children Speaker Verification0
Diversity Enhances an LLM's Performance in RAG and Long-context Task0
Matina: A Large-Scale 73B Token Persian Text Corpus0
Communication is All You Need: Persuasion Dataset Construction via Multi-LLM Communication0
Inverse problems with experiment-guided AlphaFold0
When and How Does CLIP Enable Domain and Compositional Generalization?0
Diffusion Models for Molecules: A Survey of Methods and TasksCode2
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