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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 14311440 of 9051 papers

TitleStatusHype
EEV: A Large-Scale Dataset for Studying Evoked Expressions from VideoCode1
GLAMOUR: Graph Learning over Macromolecule RepresentationsCode1
ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency DistillationCode1
ProGen: Language Modeling for Protein GenerationCode1
Adversarial Feature Hallucination Networks for Few-Shot LearningCode1
CIC: Contrastive Intrinsic Control for Unsupervised Skill DiscoveryCode1
HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image ModelsCode1
ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score DistillationCode1
Illuminating Mario Scenes in the Latent Space of a Generative Adversarial NetworkCode1
HQ-50K: A Large-scale, High-quality Dataset for Image RestorationCode1
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