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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 20612070 of 9051 papers

TitleStatusHype
HybridFC: A Hybrid Fact-Checking Approach for Knowledge GraphsCode0
HyperMAN: Hypergraph-enhanced Meta-learning Adaptive Network for Next POI RecommendationCode0
Illuminating the Space of Beatable Lode Runner Levels Produced By Various Generative Adversarial NetworksCode0
Indian Regional Movie Dataset for Recommender SystemsCode0
How well do you know your summarization datasets?Code0
COD3S: Diverse Generation with Discrete Semantic SignaturesCode0
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural ImagesCode0
CoCoFormer: A controllable feature-rich polyphonic music generation methodCode0
How Well Do LLMs Identify Cultural Unity in Diversity?Code0
How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning?Code0
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