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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 921930 of 9051 papers

TitleStatusHype
Linear Precoding Design for OTFS Systems in Time/Frequency Selective Fading Channels0
CNC: Cross-modal Normality Constraint for Unsupervised Multi-class Anomaly Detection0
A Method for Enhancing the Safety of Large Model Generation Based on Multi-dimensional Attack and Defense0
VoxVietnam: a Large-Scale Multi-Genre Dataset for Vietnamese Speaker Recognition0
Addressing Challenges in Data Quality and Model Generalization for Malaria Detection0
PQD: Post-training Quantization for Efficient Diffusion Models0
Intrinsic meaning, perception, and matching0
A Large-Scale Study on Video Action Dataset CondensationCode1
Enhancing Annotated Bibliography Generation with LLM Ensembles0
Toward Intelligent and Secure Cloud: Large Language Model Empowered Proactive DefenseCode1
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