SOTAVerified

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 10511060 of 9051 papers

TitleStatusHype
Florence-2: Advancing a Unified Representation for a Variety of Vision TasksCode1
Biological Sequence Design with GFlowNetsCode1
Bilingual Mutual Information Based Adaptive Training for Neural Machine TranslationCode1
FineRec:Exploring Fine-grained Sequential RecommendationCode1
Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual FeaturesCode1
Fine-Grained VR Sketching: Dataset and InsightsCode1
FLAIR: a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical ImageryCode1
Beyond Trivial Counterfactual Explanations with Diverse Valuable ExplanationsCode1
Fibottention: Inceptive Visual Representation Learning with Diverse Attention Across HeadsCode1
FFR V1.0: Fon-French Neural Machine TranslationCode1
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