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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 31013110 of 9051 papers

TitleStatusHype
AnyHome: Open-Vocabulary Generation of Structured and Textured 3D Homes0
CAD: Photorealistic 3D Generation via Adversarial Distillation0
The Journey, Not the Destination: How Data Guides Diffusion ModelsCode1
Promoting Counterfactual Robustness through DiversityCode0
A Vision for Operationalising Diversity and Inclusion in AI0
Synergizing Quality-Diversity with Descriptor-Conditioned Reinforcement LearningCode0
Singular Value Penalization and Semantic Data Augmentation for Fully Test-Time Adaptation0
Exploiting Representation Bias for Data Distillation in Abstractive Text Summarization0
Federated Learning Empowered by Generative Content0
R2-Talker: Realistic Real-Time Talking Head Synthesis with Hash Grid Landmarks Encoding and Progressive Multilayer Conditioning0
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