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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 24912500 of 9051 papers

TitleStatusHype
Gaussian Splatting Decoder for 3D-aware Generative Adversarial Networks0
Promoting the linguistic diversity of TEI in the Maghreb and the Arab region0
MAD Speech: Measures of Acoustic Diversity of Speech0
DESTEIN: Navigating Detoxification of Language Models via Universal Steering Pairs and Head-wise Activation FusionCode0
Optimization of Prompt Learning via Multi-Knowledge Representation for Vision-Language ModelsCode0
Measuring Diversity of Game Scenarios0
Taming Latent Diffusion Model for Neural Radiance Field Inpainting0
Memory Sharing for Large Language Model based AgentsCode1
in2IN: Leveraging individual Information to Generate Human INteractionsCode2
Text-Driven Diverse Facial Texture Generation via Progressive Latent-Space Refinement0
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