Solutions to LaTeX out of memory
LaTeX as a decades old system should not use too much memory. But sometimes, we
will see it run out of memory. There are various solutions to this. Here are
what I tried.
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Taylor & Letham (2018) Forecasting at Scale
This is the paper for Facebook Prophet.
It considers time series \(y(t)\) as a composition of trend, seasonality, and
holidays under generalized additive model (GAM):
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Glorot & Bengio (2010) Understanding the difficulty of training deep feedforward neural networks
This is the paper that explains what caused the gradient vanishing or exploding
problem in training neural networks. The approach was to experiment with some
fabricated image datasets as well as ImageNet datasets for multi-class
classifications. Then some theoretical derivation is provided to support the
argument.
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Thinking with type
Ellen Lupton / 2010
A nice book for leisure reading, and helps making better decisions on choice of
fonts and page layout. It has a website too: http://thinkingwithtype.com
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van der Maaten & Hinton (2008) Visualizing data using t-SNE
t-SNE is often used as a better alternative than PCA in terms of visualization.
This paper is the one that proposed it. It is an extension to SNE (stochastic
neighbor embedding), which the first few page of the paper outlined it:
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