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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 501510 of 9051 papers

TitleStatusHype
SynCity: Training-Free Generation of 3D Worlds0
Chain of Functions: A Programmatic Pipeline for Fine-Grained Chart Reasoning Data0
Probabilistic Prompt Distribution Learning for Animal Pose EstimationCode1
ScalingNoise: Scaling Inference-Time Search for Generating Infinite Videos0
Uncertainty Meets Diversity: A Comprehensive Active Learning Framework for Indoor 3D Object Detection0
LaPIG: Cross-Modal Generation of Paired Thermal and Visible Facial Images0
Unify and Triumph: Polyglot, Diverse, and Self-Consistent Generation of Unit Tests with LLMs0
Autonomous AI imitators increase diversity in homogeneous information ecosystems0
Temporal Regularization Makes Your Video Generator Stronger0
From 1,000,000 Users to Every User: Scaling Up Personalized Preference for User-level AlignmentCode1
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