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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 36513660 of 9051 papers

TitleStatusHype
Robust Fraud Detection via Supervised Contrastive Learning0
Liquid Crystal-Based RIS for VLC Transmitters: Performance Analysis, Challenges, and Opportunities0
Digital Twin-Oriented Complex Networked Systems based on Heterogeneous Node Features and Interaction Rules0
Attesting Distributional Properties of Training Data for Machine LearningCode0
Exploring Sampling Techniques for Generating Melodies with a Transformer Language Model0
Diverse Cotraining Makes Strong Semi-Supervised SegmentorCode1
LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and BenchmarkCode1
Language-guided Human Motion Synthesis with Atomic ActionsCode1
Minimum Coverage Sets for Training Robust Ad Hoc Teamwork Agents0
Text-Only Training for Visual Storytelling0
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