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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 10411050 of 9051 papers

TitleStatusHype
Forcing Diffuse Distributions out of Language ModelsCode1
Bias Loss for Mobile Neural NetworksCode1
FHDe²Net: Full High Definition Demoireing NetworkCode1
Few-Shot Video Object DetectionCode1
Beyond Trivial Counterfactual Explanations with Diverse Valuable ExplanationsCode1
FFR V1.0: Fon-French Neural Machine TranslationCode1
Fibottention: Inceptive Visual Representation Learning with Diverse Attention Across HeadsCode1
Few-Shot Medical Image Segmentation via a Region-enhanced Prototypical TransformerCode1
Beyond Performance Plateaus: A Comprehensive Study on Scalability in Speech EnhancementCode1
Few-Shot Object Detection via Synthetic Features with Optimal TransportCode1
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