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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 23912400 of 9051 papers

TitleStatusHype
Multi-Space Alignments Towards Universal LiDAR SegmentationCode2
SynFlowNet: Design of Diverse and Novel Molecules with Synthesis ConstraintsCode2
Towards Inclusive Face Recognition Through Synthetic Ethnicity Alteration0
Are We Really Achieving Better Beyond-Accuracy Performance in Next Basket Recommendation?0
Benchmarking Representations for Speech, Music, and Acoustic EventsCode2
Generative manufacturing systems using diffusion models and ChatGPT0
Multi-task Learning-based Joint CSI Prediction and Predictive Transmitter Selection for Security0
Predictive Accuracy-Based Active Learning for Medical Image SegmentationCode0
Global News Synchrony and Diversity During the Start of the COVID-19 PandemicCode0
KVP10k : A Comprehensive Dataset for Key-Value Pair Extraction in Business DocumentsCode1
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