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Found 3 papers, 2 papers with code
Learning Data Augmentation Schedules for Natural Language Processing
Despite its proven efficiency in other fields, data augmentation is less popular in the context of natural language processing (NLP) due to its complexity and limited results.
Deriving Visual Semantics from Spatial Context: An Adaptation of LSA and Word2Vec to generate Object and Scene Embeddings from Images
To this end, we formulate a distributional hypothesis for objects and scenes: Scenes that contain the same objects (object context) are semantically related.
Cross-validation in high-dimensional spaces: a lifeline for least-squares models and multi-class LDA
However, since their computation time increases cubically with the number of features, they are inefficient in high-dimensional neuroimaging datasets.
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