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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 2541–2550 of 9051 papers

TitleStatusHype
A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications—0
7 Tesla multimodal MRI dataset of ex-vivo human brain—0
CCVA-FL: Cross-Client Variations Adaptive Federated Learning for Medical Imaging—0
An Unscented Kalman Filter-Informed Neural Network for Vehicle Sideslip Angle Estimation—0
CCS: Controllable and Constrained Sampling with Diffusion Models via Initial Noise Perturbation—0
CAViaR: Context Aware Video Recommendations—0
A Comprehensive Review of Data-Driven Co-Speech Gesture Generation—0
Cause-Aware Empathetic Response Generation via Chain-of-Thought Fine-Tuning—0
Causal Learning for Heterogeneous Subgroups Based on Nonlinear Causal Kernel Clustering—0
Antithetic Noise in Diffusion Models—0
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