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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 14711480 of 9051 papers

TitleStatusHype
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from DataCode1
Controllable Multi-Interest Framework for RecommendationCode1
Controlling Behavioral Diversity in Multi-Agent Reinforcement LearningCode1
NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource LanguagesCode1
Automatic Data Augmentation for 3D Medical Image SegmentationCode1
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text GenerationCode1
AP-10K: A Benchmark for Animal Pose Estimation in the WildCode1
Object Detection in Optical Remote Sensing Images: A Survey and A New BenchmarkCode1
Adaptive Diffusion Terrain Generator for Autonomous Uneven Terrain NavigationCode1
Contrastive Syn-to-Real GeneralizationCode1
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