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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 911920 of 9051 papers

TitleStatusHype
Euler-Lagrange Analysis of Generative Adversarial NetworksCode1
BenthicNet: A global compilation of seafloor images for deep learning applicationsCode1
Diverse Human Motion Prediction Guided by Multi-Level Spatial-Temporal AnchorsCode1
DiveR-CT: Diversity-enhanced Red Teaming Large Language Model Assistants with Relaxing ConstraintsCode1
AIM-Fair: Advancing Algorithmic Fairness via Selectively Fine-Tuning Biased Models with Contextual Synthetic DataCode1
Diverse and Admissible Trajectory Forecasting through Multimodal Context UnderstandingCode1
EnvEdit: Environment Editing for Vision-and-Language NavigationCode1
Diverse and Admissible Trajectory Prediction through Multimodal Context UnderstandingCode1
Diverse Cotraining Makes Strong Semi-Supervised SegmentorCode1
BlendX: Complex Multi-Intent Detection with Blended PatternsCode1
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