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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 591–600 of 9051 papers

TitleStatusHype
Federated Learning Meets Fluid Antenna: Towards Robust and Scalable Edge Intelligence—0
Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic SegmentationCode0
ExpertGenQA: Open-ended QA generation in Specialized Domains—0
Controllable Motion Generation via Diffusion Modal CouplingCode0
Hypergraph Foundation Model—0
SampleMix: A Sample-wise Pre-training Data Mixing Strategey by Coordinating Data Quality and Diversity—0
Diversity Covariance-Aware Prompt Learning for Vision-Language Models—0
HI-Series Algorithms A Hybrid of Substance Diffusion Algorithm and Collaborative Filtering—0
Enhancing Retinal Vessel Segmentation Generalization via Layout-Aware Generative Modelling—0
Group Relative Policy Optimization for Image CaptioningCode0
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