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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 16711680 of 9051 papers

TitleStatusHype
Alchemy: A Quantum Chemistry Dataset for Benchmarking AI ModelsCode0
INSightR-Net: Interpretable Neural Network for Regression using Similarity-based Comparisons to Prototypical ExamplesCode0
Privacy-preserving datasets by capturing feature distributions with Conditional VAEsCode0
Intentional Computational Level DesignCode0
Albumentations: fast and flexible image augmentationsCode0
Relevance Attack on DetectorsCode0
Input-gradient space particle inference for neural network ensemblesCode0
A Tool for Super-Resolving Multimodal Clinical MRICode0
Abusive Language Recognition in RussianCode0
Information-Theoretic Active Learning for Content-Based Image RetrievalCode0
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