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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 921930 of 9051 papers

TitleStatusHype
Diverse Human Motion Prediction Guided by Multi-Level Spatial-Temporal AnchorsCode1
Diverse Generative Perturbations on Attention Space for Transferable Adversarial AttacksCode1
Evaluating Logical Generalization in Graph Neural NetworksCode1
Diverse Image Captioning with Context-Object Split Latent SpacesCode1
Evaluation and Efficiency Comparison of Evolutionary Algorithms for Service Placement Optimization in Fog ArchitecturesCode1
Diverse Image Generation via Self-Conditioned GANsCode1
Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human ProgrammersCode1
Diverse Policy Optimization for Structured Action SpaceCode1
Everyone Deserves A Reward: Learning Customized Human PreferencesCode1
eProduct: A Million-Scale Visual Search Benchmark to Address Product Recognition ChallengesCode1
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