Neural Ordinary Differential Equations
Neural ordinary differential equations Chen et al., NeurIPS’18
‘Neural Ordinary Differential Equations’ won a best paper award at NeurIPS last month. It’s not an easy piece (at least not for me!), but in the spirit of ‘deliberate practice’ that doesn’t mean there isn’t something to be gained from trying to understand as much as possible.
In addition to the paper itself, I found the following additional resources to be helpful:
- Thread on HN
- Kevin Gibson’s blog post on the paper
- Branislav Holländer’s post on ‘Towards Data Science
- Adam Kosiorek’s introduction to normalizing flows (as linked from the previous post)
- High level summary in MIT TR’s ‘The Algorithm’ newsletter
Neural networks as differential equations
Consider a multi-layered neural network. We have an input layer and an output layer, and inbetween them, some number of hidden layers. As an input feeds forward through the network, it is progressively transformed, one layer at a time, from the input to the ultimate output. Each network layer is a step on that journey. If we take a small number of big steps, we end up with a rough approximation to the true transformation function we’d like to learn. If we take a much Continue reading
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