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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 25812590 of 9051 papers

TitleStatusHype
Instruction-based Hypergraph Pretraining0
SubjectDrive: Scaling Generative Data in Autonomous Driving via Subject Control0
Towards Multimodal Video Paragraph Captioning Models Robust to Missing ModalityCode0
PLOT-TAL -- Prompt Learning with Optimal Transport for Few-Shot Temporal Action Localization0
Since the Scientific Literature Is Multilingual, Our Models Should Be Too0
Improving Content Recommendation: Knowledge Graph-Based Semantic Contrastive Learning for Diversity and Cold-Start Users0
Language Plays a Pivotal Role in the Object-Attribute Compositional Generalization of CLIP0
BlendX: Complex Multi-Intent Detection with Blended PatternsCode1
Don't Look into the Dark: Latent Codes for Pluralistic Image Inpainting0
SteinGen: Generating Fidelitous and Diverse Graph SamplesCode0
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