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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

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Showing 1711–1720 of 9051 papers

TitleStatusHype
AI-enhanced Collective IntelligenceCode0
InfoDiffusion: Information Entropy Aware Diffusion Process for Non-Autoregressive Text GenerationCode0
Result Diversification in Search and Recommendation: A SurveyCode0
A Survey of Data Synthesis ApproachesCode0
About Explicit Variance Minimization: Training Neural Networks for Medical Imaging With Limited Data AnnotationsCode0
Information Density Principle for MLLM BenchmarksCode0
Input-gradient space particle inference for neural network ensemblesCode0
Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic ManuscriptsCode0
Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic RegressionCode0
In-distribution Public Data Synthesis with Diffusion Models for Differentially Private Image ClassificationCode0
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