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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 901910 of 9051 papers

TitleStatusHype
Adding Seemingly Uninformative Labels Helps in Low Data RegimesCode1
Learning to Transform Dynamically for Better Adversarial TransferabilityCode1
Evaluating the Evaluation of Diversity in Natural Language GenerationCode1
Less is More: Learning from Synthetic Data with Fine-grained Attributes for Person Re-IdentificationCode1
Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros StudyCode1
Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill DiversityCode1
Distribution-aware Knowledge Prototyping for Non-exemplar Lifelong Person Re-identificationCode1
LGD-GCN: Local and Global Disentangled Graph Convolutional NetworksCode1
Distributed speech separation in spatially unconstrained microphone arraysCode1
EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text ClassificationCode1
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