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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 10011010 of 9051 papers

TitleStatusHype
Neuro-evolutionary evidence for a universal fractal primate brain shapeCode1
Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill DiscoveryCode1
DiffuseExpand: Expanding dataset for 2D medical image segmentation using diffusion modelsCode1
Noise Conditional Flow Model for Learning the Super-Resolution SpaceCode1
AlpaCare:Instruction-tuned Large Language Models for Medical ApplicationCode1
NoisyTwins: Class-Consistent and Diverse Image Generation through StyleGANsCode1
Analyzing Generalization of Vision and Language Navigation to Unseen Outdoor AreasCode1
Continual Object Detection via Prototypical Task Correlation Guided Gating MechanismCode1
AlphaFold Distillation for Protein DesignCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
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