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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 12711280 of 9051 papers

TitleStatusHype
Automating Rigid Origami DesignCode1
Can 3D Vision-Language Models Truly Understand Natural Language?Code1
Deep Sketch-Based Modeling: Tips and TricksCode1
BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face GenerationCode1
Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time seriesCode1
BlendX: Complex Multi-Intent Detection with Blended PatternsCode1
DEFN: Dual-Encoder Fourier Group Harmonics Network for Three-Dimensional Indistinct-Boundary Object SegmentationCode1
GenPlot: Increasing the Scale and Diversity of Chart Derendering DataCode1
G-Eval: NLG Evaluation using GPT-4 with Better Human AlignmentCode1
Group-wise Contrastive Learning for Neural Dialogue GenerationCode1
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