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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 32913300 of 9051 papers

TitleStatusHype
PRISM: Progressive Restoration for Scene Graph-based Image Manipulation0
Carrier Frequency Offset Estimation for OCDM with Null Subchirps0
Domain Randomization via Entropy Maximization0
Mix-ME: Quality-Diversity for Multi-Agent Learning0
Data-Free Distillation of Language Model by Text-to-Text Transfer0
Data-Centric Long-Tailed Image Recognition0
Resilient Multiple Choice Learning: A learned scoring scheme with application to audio scene analysisCode1
Task-Agnostic Low-Rank Adapters for Unseen English DialectsCode0
Statistical Results of Multivariate Fox-H Function for Exact Performance Analysis of RIS-Assisted Wireless Communication0
NOD-TAMP: Generalizable Long-Horizon Planning with Neural Object Descriptors0
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