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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 1821–1830 of 9051 papers

TitleStatusHype
Assessment of Practical Smart Gateway Diversity Based on Multi-Site Measurements in Q/V band—0
Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification—0
Contrastive Learning for Diverse Disentangled Foreground Generation—0
Contrastive Learning from Synthetic Audio Doppelgängers—0
Embracing Diversity: A Multi-Perspective Approach with Soft Labels—0
Deep Active Learning for Multi-Label Classification of Remote Sensing Images—0
Contrastive Positive Mining for Unsupervised 3D Action Representation Learning—0
Deep Active Learning in the Presence of Label Noise: A Survey—0
Contrastive Speaker-Aware Learning for Multi-party Dialogue Generation with LLMs—0
Deep Concept Identification for Generative Design—0
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