A Marie Sklodowska-Curie ITN funded by the Horizon2020 program of the European Commission



Hyper-parameters revisited

by Giles Strong


Well folks, it’s been quite a while since my last post; apologies for that, it’s been a busy few months recently.

Towards the end of last year I wrote a post on optimising the hyper parameters (depth, width, learning rate, et cetera) of neural networks. In this post I described how I was trying to use Bayesian methods to ‘quickly’ find useful sets of parameters. Continue reading “Hyper-parameters revisited”

Adjusting hyper-parameters: First step into Bayesian optimisation of DNNs

by Giles Strong

A few months ago I wrote about some work I was doing on improving the way a certain kind of particle is detected at CMS, by replacing the existing algorithm with a neural network. I recently resumed this work and have now got to the point where I show significant improvement over the existing method. The design of the neural network, however, was one that I imported from some other work, and what I want to do is to adjust it to better suit my problem. Continue reading “Adjusting hyper-parameters: First step into Bayesian optimisation of DNNs”

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