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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 16811690 of 9051 papers

TitleStatusHype
MarsCode Agent: AI-native Automated Bug Fixing0
Kullback-Leibler cluster entropy to quantify volatility correlation and risk diversity0
An adaptative differential evolution with enhanced diversity and restart mechanismCode0
Seeing Your Speech Style: A Novel Zero-Shot Identity-Disentanglement Face-based Voice Conversion0
Rethinking Image Super-Resolution from Training Data PerspectivesCode1
TSO: Self-Training with Scaled Preference Optimization0
Data Augmentation for Image Classification using Generative AI0
LLMs Prompted for Graphs: Hallucinations and Generative Capabilities0
DiverseDialogue: A Methodology for Designing Chatbots with Human-Like Diversity0
Sparse Uncertainty-Informed Sampling from Federated Streaming DataCode0
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