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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 571580 of 9051 papers

TitleStatusHype
DivClust: Controlling Diversity in Deep ClusteringCode1
Diffusion Reward: Learning Rewards via Conditional Video DiffusionCode1
DiffuSum: Generation Enhanced Extractive Summarization with DiffusionCode1
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask LearningCode1
DiffWave: A Versatile Diffusion Model for Audio SynthesisCode1
Diffusion for Out-of-Distribution Detection on Road Scenes and BeyondCode1
Bayesian Adversarial Human Motion SynthesisCode1
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural NetworksCode1
DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic DiversityCode1
DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial NetworkCode1
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