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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 3841–3850 of 9051 papers

TitleStatusHype
CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation FrameworkCode1
A Novel Counterfactual Data Augmentation Method for Aspect-Based Sentiment Analysis—0
Lingua Manga: A Generic Large Language Model Centric System for Data Curation—0
GenPlot: Increasing the Scale and Diversity of Chart Derendering DataCode1
Optimal Execution Using Reinforcement Learning—0
Knowledge Transfer for Dynamic Multi-objective Optimization with a Changing Number of Objectives—0
Perturbation-Based Two-Stage Multi-Domain Active Learning—0
Chaotic turnover of rare and abundant species in a strongly interacting model community—0
Forest Parameter Prediction by Multiobjective Deep Learning of Regression Models Trained with Pseudo-Target ImputationCode0
Conditional Text Image Generation with Diffusion Models—0
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