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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 34213430 of 9051 papers

TitleStatusHype
Active Simultaneously Transmitting and Reflecting (STAR)-RISs: Modelling and Analysis0
Contrastive Speaker-Aware Learning for Multi-party Dialogue Generation with LLMs0
Asymmetric double-winged multi-view clustering network for exploring Diverse and Consistent Information0
Contrastive Positive Mining for Unsupervised 3D Action Representation Learning0
A Swiss German Dictionary: Variation in Speech and Writing0
A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect0
Contrastive Learning from Synthetic Audio Doppelgängers0
Contrastive Learning for Diverse Disentangled Foreground Generation0
A survey on the impact of AI-based recommenders on human behaviours: methodologies, outcomes and future directions0
AI in Support of Diversity and Inclusion0
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