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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 24612470 of 9051 papers

TitleStatusHype
Beyond Attentive Tokens: Incorporating Token Importance and Diversity for Efficient Vision TransformersCode0
GAN-GA: A Generative Model based on Genetic Algorithm for Medical Image GenerationCode0
"Garbage In, Garbage Out" Revisited: What Do Machine Learning Application Papers Report About Human-Labeled Training Data?Code0
GAIT: A Geometric Approach to Information TheoryCode0
Beyond a Single Mode: GAN Ensembles for Diverse Medical Data GenerationCode0
G2GT: Retrosynthesis Prediction with Graph to Graph Attention Neural Network and Self-TrainingCode0
Discovering Many Diverse Solutions with Bayesian OptimizationCode0
Beyond Task Diversity: Provable Representation Transfer for Sequential Multi-Task Linear BanditsCode0
Beyond Aesthetics: Cultural Competence in Text-to-Image ModelsCode0
Sequential Recommendation with Controllable Diversification: Representation Degeneration and DiversityCode0
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