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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 14111420 of 9051 papers

TitleStatusHype
MAP-Elites based Hyper-Heuristic for the Resource Constrained Project Scheduling ProblemCode1
Mapping Researchers with PeopleMapCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
Masked Generative Modeling with Enhanced Sampling SchemeCode1
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text GenerationCode1
Controlling Behavioral Diversity in Multi-Agent Reinforcement LearningCode1
Maximum Entropy Population-Based Training for Zero-Shot Human-AI CoordinationCode1
CAPIVARA: Cost-Efficient Approach for Improving Multilingual CLIP Performance on Low-Resource LanguagesCode1
MDCS: More Diverse Experts with Consistency Self-distillation for Long-tailed RecognitionCode1
Cross-Domain Feature Augmentation for Domain GeneralizationCode1
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