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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

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Showing 3061–3070 of 9051 papers

TitleStatusHype
XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAXCode2
From Good to Great: Improving Math Reasoning with Tool-Augmented Interleaf Prompting—0
Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity—0
Domain adaption and physical constrains transfer learning for shale gas production—0
Density Descent for Diversity Optimization—0
A Multimodal Approach for Advanced Pest Detection and Classification—0
Few-Shot Learning from Augmented Label-Uncertain Queries in Bongard-HOI—0
K-ESConv: Knowledge Injection for Emotional Support Dialogue Systems via Prompt Learning—0
Sails and Anchors: The Complementarity of Exploratory and Exploitative Scientists in Knowledge Creation—0
Rethinking Robustness of Model AttributionsCode0
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