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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 3641–3650 of 9051 papers

TitleStatusHype
FedDAR: Federated Domain-Aware Representation Learning—0
FedEPA: Enhancing Personalization and Modality Alignment in Multimodal Federated Learning—0
Conceptual capacity and effective complexity of neural networks—0
Federated Data Model—0
Federated Learning Empowered by Generative Content—0
Federated Learning for Emoji Prediction in a Mobile Keyboard—0
A high fidelity synthetic face framework for computer vision—0
Federated Learning Meets Fluid Antenna: Towards Robust and Scalable Edge Intelligence—0
Federated Learning for Personalized Humor Recognition—0
Exploring the Design Space of Diffusion Bridge Models via Stochasticity Control—0
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