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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 681690 of 9051 papers

TitleStatusHype
Neural Multi-Objective Combinatorial Optimization with Diversity EnhancementCode1
FLAIR: a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical ImageryCode1
CAPIVARA: Cost-Efficient Approach for Improving Multilingual CLIP Performance on Low-Resource LanguagesCode1
Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion ModelsCode1
AutoMix: Automatically Mixing Language ModelsCode1
Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine LearningCode1
Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time seriesCode1
PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly DetectionCode1
Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven OptimizationCode1
MixEdit: Revisiting Data Augmentation and Beyond for Grammatical Error CorrectionCode1
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