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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 29012910 of 9051 papers

TitleStatusHype
Deep Ensembles Work, But Are They Necessary?Code0
Exploring Format Consistency for Instruction TuningCode0
Exploring Precision and Recall to assess the quality and diversity of LLMsCode0
Fast and Functional Structured Data Generators Rooted in Out-of-Equilibrium PhysicsCode0
Fine-Grained Detoxification via Instance-Level Prefixes for Large Language ModelsCode0
Generating Automatically Print/Scan Textures for Morphing Attack Detection ApplicationsCode0
Deep Ensembles with Hierarchical Diversity PruningCode0
Exploiting ConvNet Diversity for Flooding IdentificationCode0
Exploratory State Representation LearningCode0
Explaining crime diversity with Google street viewCode0
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