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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 43514360 of 9051 papers

TitleStatusHype
Diverse Human Motion Prediction Guided by Multi-Level Spatial-Temporal AnchorsCode1
Sample-efficient Multi-objective Molecular Optimization with GFlowNetsCode1
Why the Mansfield Rule can't work: a supply demand analysis0
Performative Recommendation: Diversifying Content via Strategic IncentivesCode0
Cluster Index Modulation for Reconfigurable Intelligent Surface-Assisted mmWave Massive MIMO0
MMPD: Multi-Domain Mobile Video Physiology DatasetCode1
Mask Conditional Synthetic Satellite ImageryCode1
A Vector Quantized Approach for Text to Speech Synthesis on Real-World Spontaneous SpeechCode2
Effective Data Augmentation With Diffusion ModelsCode2
Ethical Considerations for Responsible Data CurationCode0
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