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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 881890 of 9051 papers

TitleStatusHype
DifFlow3D: Toward Robust Uncertainty-Aware Scene Flow Estimation with Diffusion ModelCode1
Evaluating the Evaluation of Diversity in Natural Language GenerationCode1
Euler-Lagrange Analysis of Generative Adversarial NetworksCode1
Evaluation and Efficiency Comparison of Evolutionary Algorithms for Service Placement Optimization in Fog ArchitecturesCode1
Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human ProgrammersCode1
EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text ClassificationCode1
Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity RecognitionCode1
Adding Seemingly Uninformative Labels Helps in Low Data RegimesCode1
EnvEdit: Environment Editing for Vision-and-Language NavigationCode1
eProduct: A Million-Scale Visual Search Benchmark to Address Product Recognition ChallengesCode1
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