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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 861870 of 9051 papers

TitleStatusHype
Supervision policies can shape long-term risk management in general-purpose AI modelsCode0
Poetry in Pixels: Prompt Tuning for Poem Image Generation via Diffusion ModelsCode0
Bridging Dialects: Translating Standard Bangla to Regional Variants Using Neural Models0
Ultrasound Image Synthesis Using Generative AI for Lung Ultrasound Detection0
EquiBoost: An Equivariant Boosting Approach to Molecular Conformation GenerationCode0
Arc2Avatar: Generating Expressive 3D Avatars from a Single Image via ID Guidance0
Consistent Flow Distillation for Text-to-3D Generation0
Learning Compact and Robust Representations for Anomaly Detection0
Seeing Sound: Assembling Sounds from Visuals for Audio-to-Image Generation0
Tuning-Free Long Video Generation via Global-Local Collaborative Diffusion0
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