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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 891900 of 9051 papers

TitleStatusHype
Evaluating Logical Generalization in Graph Neural NetworksCode1
DiffWave: A Versatile Diffusion Model for Audio SynthesisCode1
Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human ProgrammersCode1
Diffusion Reward: Learning Rewards via Conditional Video DiffusionCode1
Evaluating the Evaluation of Diversity in Natural Language GenerationCode1
DirectMultiStep: Direct Route Generation for Multi-Step RetrosynthesisCode1
DivAug: Plug-in Automated Data Augmentation with Explicit Diversity MaximizationCode1
Diversify Your Vision Datasets with Automatic Diffusion-Based AugmentationCode1
Toward a Plug-and-Play Vision-Based Grasping Module for RoboticsCode1
Adding Seemingly Uninformative Labels Helps in Low Data RegimesCode1
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