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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

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Showing 4281–4290 of 9051 papers

TitleStatusHype
Beyond Conservatism: Diffusion Policies in Offline Multi-agent Reinforcement Learning—0
Diffusion Model for Slate Recommendation—0
An Empirical Bayes Framework for Open-Domain Dialogue Generation—0
Diffusion Model Conditioning on Gaussian Mixture Model and Negative Gaussian Mixture Gradient—0
A ground-based dataset and a diffusion model for on-orbit low-light image enhancement—0
Beyond Comparing Image Pairs: Setwise Active Learning for Relative Attributes—0
A Decomposition-Based Many-Objective Evolutionary Algorithm with Local Iterative Update—0
Diffusion Meets Options: Hierarchical Generative Skill Composition for Temporally-Extended Tasks—0
Diffusion-like recommendation with enhanced similarity of objects—0
Diffusion Least Mean Square: Simulations—0
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