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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 20812090 of 9051 papers

TitleStatusHype
How Predictable Are Large Language Model Capabilities? A Case Study on BIG-benchCode0
Towards control of opinion diversity by introducing zealots into a polarised social groupCode0
How to partition diversityCode0
How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning?Code0
How Far Can We Extract Diverse Perspectives from Large Language Models?Code0
How Good Are Synthetic Requirements ? Evaluating LLM-Generated Datasets for AI4RECode0
How Inclusively do LMs Perceive Social and Moral Norms?Code0
FeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-spoofingCode0
Clubmark: a Parallel Isolation Framework for Benchmarking and Profiling Clustering Algorithms on NUMA ArchitecturesCode0
CLR-Wire: Towards Continuous Latent Representations for 3D Curve Wireframe GenerationCode0
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