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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 16311640 of 9051 papers

TitleStatusHype
Intentional Computational Level DesignCode0
Alleviating the Long-Tail Problem in Conversational Recommender SystemsCode0
Interactive Image Segmentation With Latent DiversityCode0
Integrating LLMs and Decision Transformers for Language Grounded Generative Quality-DiversityCode0
Integrating Present and Past in Unsupervised Continual LearningCode0
Augmenting High-dimensional Nonlinear Optimization with Conditional GANsCode0
Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical SpaceCode0
Intent Factored Generation: Unleashing the Diversity in Your Language ModelCode0
Interactive Neural Style Transfer with ArtistsCode0
Augmented Shortcuts for Vision TransformersCode0
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