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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 55515560 of 9051 papers

TitleStatusHype
Table-to-Text Generation with Pretrained Diffusion Models0
TaCIE: Enhancing Instruction Comprehension in Large Language Models through Task-Centred Instruction Evolution0
Tackling Bias in Pre-trained Language Models: Current Trends and Under-represented Societies0
Tailor: An Integrated Text-Driven CG-Ready Human and Garment Generation System0
Tailoring Generative Adversarial Networks for Smooth Airfoil Design0
Taking the Scenic Route: Automatic Exploration for Videogames0
Taming and Leveraging Interference in Mobile Radar Networks0
Taming Latent Diffusion Model for Neural Radiance Field Inpainting0
Taming Repetition in Dialogue Generation0
TANGLED: Generating 3D Hair Strands from Images with Arbitrary Styles and Viewpoints0
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