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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 14811490 of 9051 papers

TitleStatusHype
Class-Balancing Diffusion ModelsCode1
ReSurgSAM2: Referring Segment Anything in Surgical Video via Credible Long-term TrackingCode1
Rethinking Fano's Inequality in Ensemble LearningCode1
EnvEdit: Environment Editing for Vision-and-Language NavigationCode1
EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text ClassificationCode1
eProduct: A Million-Scale Visual Search Benchmark to Address Product Recognition ChallengesCode1
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from DataCode1
Rethinking Parameter Counting in Deep Models: Effective Dimensionality RevisitedCode1
Ape210K: A Large-Scale and Template-Rich Dataset of Math Word ProblemsCode1
Improve Student's Reasoning Generalizability through Cascading Decomposed CoTs DistillationCode1
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