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Domain Agnostic Few-Shot Learning For Document Intelligence
In this work, we address the problem of few-shot document image classification under domain shift.
Efficient Document Image Classification Using Region-Based Graph Neural Network
Recent advancements in large pre-trained computer vision and language models and graph neural networks has lent document image classification many tools.
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Document Image Classification
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Weighting vectors for machine learning: numerical harmonic analysis applied to boundary detection
Metric space magnitude, an active field of research in algebraic topology, is a scalar quantity that summarizes the effective number of distinct points that live in a general metric space.
Geometric feature performance under downsampling for EEG classification tasks
We experimentally investigate a collection of feature engineering pipelines for use with a CNN for classifying eyes-open or eyes-closed from electroencephalogram (EEG) time-series from the Bonn dataset.
Simplicial 2-Complex Convolutional Neural Networks
Recently, neural network architectures have been developed to accommodate when the data has the structure of a graph or, more generally, a hypergraph.
Practical applications of metric space magnitude and weighting vectors
In a more general setting, the magnitude of a metric space is a real number that aims to quantify the effective number of distinct points in the space.
