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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 301310 of 9051 papers

TitleStatusHype
Explaining Machine Learning Classifiers through Diverse Counterfactual ExplanationsCode2
Lenia - Biology of Artificial LifeCode2
Exploring Design of Multi-Agent LLM Dialogues for Research IdeationCode1
Prompt-Free Conditional Diffusion for Multi-object Image AugmentationCode1
Inv-Entropy: A Fully Probabilistic Framework for Uncertainty Quantification in Language ModelsCode1
Scalable and Cost-Efficient de Novo Template-Based Molecular GenerationCode1
Diversity-Guided MLP Reduction for Efficient Large Vision TransformersCode1
AMPED: Adaptive Multi-objective Projection for balancing Exploration and skill DiversificationCode1
ByteMorph: Benchmarking Instruction-Guided Image Editing with Non-Rigid MotionsCode1
Foundation Molecular Grammar: Multi-Modal Foundation Models Induce Interpretable Molecular Graph LanguagesCode1
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