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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 381390 of 9051 papers

TitleStatusHype
Curiosity-Driven Reinforcement Learning from Human FeedbackCode1
CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image SteganographyCode1
Cross-Covariate Gait Recognition: A BenchmarkCode1
Cross-Domain Feature Augmentation for Domain GeneralizationCode1
CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and OptimizationCode1
TransferTOD: A Generalizable Chinese Multi-Domain Task-Oriented Dialogue System with Transfer CapabilitiesCode1
Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised SegmentationCode1
Curriculum-guided Hindsight Experience ReplayCode1
AcroFOD: An Adaptive Method for Cross-domain Few-shot Object DetectionCode1
AARGH! End-to-end Retrieval-Generation for Task-Oriented DialogCode1
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