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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 541550 of 9051 papers

TitleStatusHype
SCONE: A Novel Stochastic Sampling to Generate Contrastive Views and Hard Negative Samples for RecommendationCode1
Modeling Caption Diversity in Contrastive Vision-Language PretrainingCode1
Soft Prompt Generation for Domain GeneralizationCode1
CompilerDream: Learning a Compiler World Model for General Code OptimizationCode1
Elucidating the Design Space of Dataset CondensationCode1
FineRec:Exploring Fine-grained Sequential RecommendationCode1
MambaPupil: Bidirectional Selective Recurrent model for Event-based Eye trackingCode1
Forcing Diffuse Distributions out of Language ModelsCode1
Memory Sharing for Large Language Model based AgentsCode1
How Consistent are Clinicians? Evaluating the Predictability of Sepsis Disease Progression with Dynamics ModelsCode1
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