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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 391400 of 9051 papers

TitleStatusHype
Using LLMs as prompt modifier to avoid biases in AI image generators0
Controllable Expressive 3D Facial Animation via Diffusion in a Unified Multimodal Space0
TAMP: Token-Adaptive Layerwise Pruning in Multimodal Large Language ModelsCode1
Relation-Rich Visual Document Generator for Visual Information ExtractionCode0
The Impact of Model Zoo Size and Composition on Weight Space LearningCode0
Accelerating Differentially Private Federated Learning via Adaptive Extrapolation0
Can genomic analysis actually estimate past population size?0
Weight Ensembling Improves Reasoning in Language Models0
Diversity Analysis for Indoor Terahertz Communication Systems under Small-Scale Fading0
Diversity-Fair Online Selection0
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