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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 13211330 of 9051 papers

TitleStatusHype
DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic DiversityCode1
Mitigating Gender Bias for Neural Dialogue Generation with Adversarial LearningCode1
Advanced Codebook Design for SCMA-aided NTNs With Randomly Distributed UsersCode1
MixEdit: Revisiting Data Augmentation and Beyond for Grammatical Error CorrectionCode1
Goals as Reward-Producing ProgramsCode1
MMA Regularization: Decorrelating Weights of Neural Networks by Maximizing the Minimal AnglesCode1
Can we use Common Voice to train a Multi-Speaker TTS system?Code1
Towards Geospatial Foundation Models via Continual PretrainingCode1
Mitigating Open-Vocabulary Caption HallucinationsCode1
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problemCode1
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