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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 751760 of 9051 papers

TitleStatusHype
ZYN: Zero-Shot Reward Models with Yes-No Questions for RLAIFCode1
Quality Diversity under Sparse Reward and Sparse Interaction: Application to Grasping in RoboticsCode1
From Sky to the Ground: A Large-scale Benchmark and Simple Baseline Towards Real Rain RemovalCode1
Path Shadowing Monte-CarloCode1
Deep Image Harmonization with Learnable AugmentationCode1
Human-M3: A Multi-view Multi-modal Dataset for 3D Human Pose Estimation in Outdoor ScenesCode1
LatentAugment: Data Augmentation via Guided Manipulation of GAN's Latent SpaceCode1
Kick Back & Relax: Learning to Reconstruct the World by Watching SlowTVCode1
Towards Task Sampler Learning for Meta-LearningCode1
Rumor Detection with Diverse Counterfactual EvidenceCode1
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