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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 3661–3670 of 9051 papers

TitleStatusHype
Exploring structure diversity in atomic resolution microscopy with graph neural networks—0
Exploring Story Generation with Multi-task Objectives in Variational Autoencoders—0
Conceptors: an easy introduction—0
Feint Behaviors and Strategies: Formalization, Implementation and Evaluation—0
Exploring Sampling Techniques for Generating Melodies with a Transformer Language Model—0
Concept-Monitor: Understanding DNN training through individual neurons—0
Few-shot 3D Shape Generation—0
Few-Shot Airway-Tree Modeling using Data-Driven Sparse Priors—0
Domain-Agnostic Few-Shot Classification by Learning Disparate Modulators—0
A Simulation Study of Bandit Algorithms to Address External Validity of Software Fault Prediction—0
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