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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 1811–1820 of 9051 papers

TitleStatusHype
Constraint-based Diversification of JOP Gadgets—0
Continual Reinforcement Learning with Diversity Exploration and Adversarial Self-Correction—0
Continual Self-supervised Learning Considering Medical Domain Knowledge in Chest CT Images—0
Continual Semantic Segmentation with Automatic Memory Sample Selection—0
Continual Speaker Adaptation for Text-to-Speech Synthesis—0
Assessment of Practical Smart Gateway Diversity Based on Multi-Site Measurements in Q/V band—0
Continuous Inference in Graphical Models with Polynomial Energies—0
Continuously Discovering Novel Strategies via Reward-Switching Policy Optimization—0
A Survey on Long-Video Storytelling Generation: Architectures, Consistency, and Cinematic Quality—0
Embracing Diversity: A Multi-Perspective Approach with Soft Labels—0
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