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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 25912600 of 9051 papers

TitleStatusHype
A Novel Multiple Interval Prediction Method for Electricity Prices based on Scenarios Generation: Definition and Method0
Capturing the Production of the Innovative Ideas: An Online Social Network Experiment and "Idea Geography" Visualization0
Adversarial Environment Design via Regret-Guided Diffusion Models0
Scenarios Generation-based Multiple Interval Prediction Method for Electricity Prices0
Capturing the diversity of biological tuning curves using generative adversarial networks0
A Novel Multi-Objective Velocity-Free Boolean Particle Swarm Optimization0
Capturing Bias Diversity in LLMs0
Captioning Images Taken by People Who Are Blind0
Distributionally-Informed Recommender System Evaluation0
``Caption'' as a Coherence Relation: Evidence and Implications0
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