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Statistical and Algorithmic Insights for Semi-supervised Learning with Self-training
We then establish a connection between self-training based semi-supervision and the more general problem of learning with heterogenous data and weak supervision.
Stronger Convergence Results for Deep Residual Networks: Network Width Scales Linearly with Training Data Size
Deep neural networks are highly expressive machine learning models with the ability to interpolate arbitrary datasets.
Comments on "Deep Neural Networks with Random Gaussian Weights: A Universal Classification Strategy?"
In a recently published paper [1], it is shown that deep neural networks (DNNs) with random Gaussian weights preserve the metric structure of the data, with the property that the distance shrinks more when the angle between the two data points is smaller.
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