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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 941950 of 9051 papers

TitleStatusHype
Maximum Entropy Population-Based Training for Zero-Shot Human-AI CoordinationCode1
MC^2: Towards Transparent and Culturally-Aware NLP for Minority Languages in ChinaCode1
Explain Me the Painting: Multi-Topic Knowledgeable Art Description GenerationCode1
Diversify Your Vision Datasets with Automatic Diffusion-Based AugmentationCode1
Exclusive Hierarchical Decoding for Deep Keyphrase GenerationCode1
Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros StudyCode1
Explicit Syntactic Guidance for Neural Text GenerationCode1
EvolGAN: Evolutionary Generative Adversarial NetworksCode1
Diversity-aware Channel Pruning for StyleGAN CompressionCode1
Everyone Deserves A Reward: Learning Customized Human PreferencesCode1
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