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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 49514960 of 9051 papers

TitleStatusHype
Regularized Submodular Maximization at Scale0
Regularizing Dialogue Generation by Imitating Implicit Scenarios0
Regular Time-series Generation using SGM0
Regurgitative Training: The Value of Real Data in Training Large Language Models0
Reimagining Speech: A Scoping Review of Deep Learning-Powered Voice Conversion0
Reinforced Hybrid Genetic Algorithm for the Traveling Salesman Problem0
Reinforcement-based Display-size Selection for Frugal Satellite Image Change Detection0
Reinforcement-based frugal learning for satellite image change detection0
Reinforcement Learning based QoS/QoE-aware Service Function Chaining in Software-Driven 5G Slices0
Reinforcement Learning for Sequence Design Leveraging Protein Language Models0
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