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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 17611770 of 9051 papers

TitleStatusHype
Increasing diversity of omni-directional images generated from single image using cGAN based on MLPMixerCode0
Indian Regional Movie Dataset for Recommender SystemsCode0
InclusiveFaceNet: Improving Face Attribute Detection with Race and Gender DiversityCode0
IndicEval-XL: Bridging Linguistic Diversity in Code Generation Across Indic LanguagesCode0
Improving the Evaluation of Generative Models with Fuzzy LogicCode0
Input-gradient space particle inference for neural network ensemblesCode0
Improving the Diversity of Unsupervised Paraphrasing with Embedding OutputsCode0
Concept-as-Tree: Synthetic Data is All You Need for VLM PersonalizationCode0
ComSD: Balancing Behavioral Quality and Diversity in Unsupervised Skill DiscoveryCode0
Improving the Data Efficiency of Multi-Objective Quality-Diversity through Gradient Assistance and Crowding ExplorationCode0
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