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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 49714980 of 9051 papers

TitleStatusHype
On the Usefulness of Deep Ensemble Diversity for Out-of-Distribution DetectionCode0
Neural Data-to-Text Generation Based on Small Datasets: Comparing the Added Value of Two Semi-Supervised Learning Approaches on Top of a Large Language Model0
D-CBRS: Accounting For Intra-Class Diversity in Continual Learning0
River Surface Patch-wise Detector Using Mixture Augmentation for Scum-cover-index0
Fuse It More Deeply! A Variational Transformer with Layer-Wise Latent Variable Inference for Text GenerationCode1
Diversity-aware social robots meet people: beyond context-aware embodied AI0
Causal Conceptions of Fairness and their Consequences0
IDEA: Increasing Text Diversity via Online Multi-Label Recognition for Vision-Language Pre-trainingCode0
DGPO: Discovering Multiple Strategies with Diversity-Guided Policy OptimizationCode1
Frequency Domain Model Augmentation for Adversarial AttackCode1
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