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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 15911600 of 9051 papers

TitleStatusHype
AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document SummarizationCode1
FS6D: Few-Shot 6D Pose Estimation of Novel ObjectsCode1
Few-shot Image Generation with Mixup-based Distance LearningCode1
SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement LearningCode1
Automated Filtering of Human Feedback Data for Aligning Text-to-Image Diffusion ModelsCode0
A Machine Learning Case Study for AI-empowered echocardiography of Intensive Care Unit Patients in low- and middle-income countriesCode0
Intent Factored Generation: Unleashing the Diversity in Your Language ModelCode0
Adaptive Combination of a Genetic Algorithm and Novelty Search for Deep NeuroevolutionCode0
Intentional Computational Level DesignCode0
Interactive Constrained MAP-Elites: Analysis and Evaluation of the Expressiveness of the Feature DimensionsCode0
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