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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 28112820 of 9051 papers

TitleStatusHype
Face Manifold: Manifold Learning for Synthetic Face GenerationCode0
FaceCoresetNet: Differentiable Coresets for Face Set RecognitionCode0
Facilitating bootstrapped and rarefaction-based microbiome diversity analysis with q2-bootsCode0
DeepPath: A Reinforcement Learning Method for Knowledge Graph ReasoningCode0
DeepPatent2: A Large-Scale Benchmarking Corpus for Technical Drawing UnderstandingCode0
Fact-or-Fair: A Checklist for Behavioral Testing of AI Models on Fairness-Related QueriesCode0
An adaptative differential evolution with enhanced diversity and restart mechanismCode0
Can Score-Based Generative Modeling Effectively Handle Medical Image Classification?Code0
Exploring the Role of Node Diversity in Directed Graph Representation LearningCode0
BAL: Balancing Diversity and Novelty for Active LearningCode0
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