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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 691700 of 9051 papers

TitleStatusHype
An Optimistic Perspective on Offline Deep Reinforcement LearningCode1
Diverse Image Captioning with Context-Object Split Latent SpacesCode1
COVIDx CT-3: A Large-scale, Multinational, Open-Source Benchmark Dataset for Computer-aided COVID-19 Screening from Chest CT ImagesCode1
Diverse Policy Optimization for Structured Action SpaceCode1
Diverse Video Generation using a Gaussian Process TriggerCode1
Diverse Weight Averaging for Out-of-Distribution GeneralizationCode1
Diversified Batch Selection for Training AccelerationCode1
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
Diversifying Dialog Generation via Adaptive Label SmoothingCode1
DeepFacePencil: Creating Face Images from Freehand SketchesCode1
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