SOTAVerified

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 651660 of 9051 papers

TitleStatusHype
CoT-ICL Lab: A Petri Dish for Studying Chain-of-Thought Learning from In-Context DemonstrationsCode1
Cross-Covariate Gait Recognition: A BenchmarkCode1
An Empirical Study On Contrastive Search And Contrastive Decoding For Open-ended Text GenerationCode1
An Empirical Study of Vehicle Re-Identification on the AI City ChallengeCode1
Controllable Video Captioning with an Exemplar SentenceCode1
Controllable Open-ended Question Generation with A New Question Type OntologyCode1
Controllable Multi-Interest Framework for RecommendationCode1
An Informative Tracking BenchmarkCode1
DGCN: Diversified Recommendation with Graph Convolutional NetworksCode1
Controllable Text Generation via Probability Density Estimation in the Latent SpaceCode1
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