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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 10311040 of 9051 papers

TitleStatusHype
Bilingual Mutual Information Based Adaptive Training for Neural Machine TranslationCode1
Few-Shot Medical Image Segmentation via a Region-enhanced Prototypical TransformerCode1
Few-Shot Physically-Aware Articulated Mesh Generation via Hierarchical DeformationCode1
Beyond Trivial Counterfactual Explanations with Diverse Valuable ExplanationsCode1
Analysis of diversity-accuracy tradeoff in image captioningCode1
BlendX: Complex Multi-Intent Detection with Blended PatternsCode1
DebateQA: Evaluating Question Answering on Debatable KnowledgeCode1
Analysis and Evaluation of Synthetic Data Generation in Speech Dysfluency DetectionCode1
Biological Sequence Design with GFlowNetsCode1
Few-shot Image Generation via Cross-domain CorrespondenceCode1
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