A recurring movement within deep learning research which tries to render the learning of prediction functions tractable by considering them as dynamical systems, and using the theory of stability in the context of Hamiltonians,r optimal control and/or ODE solvers, to make it all work.

I’ve been interested by this since seeing the Haber and Ruthotto paper, but it’s got a real kick recently since the Vector Institute team’s paper won the prize at NeurIPS for learning the ODEs themselves.

# Stability of training

Related, but not quite the same, notion of stability, as in data-stability in learning. Arguing that neural networks are in the limit approximants to quadrature solutions of certain ODES, work and gain insights and new tricks into neural nets by using ODE tricks.. This is mostly what Haber and Rhutthoto et al do. ([Haber, Ruthotto, Holtham, & Jun, 2017][#HRHJ17], [Haber, Lucka, & Ruthotto, 2018][#HaLR18], [Ruthotto & Haber, 2018][#RuHa18])

## Can it work on time series?

Good question; It looks like it should, since there is an implicit time series the ODE-solver. But these problems so far have use non-time-series data.

## Neural ODE regression

By which I mean *learning an ODE whose solution is the regression problem*. This is what e.g. the famouse Vector Institute paper did, although I’m not sure its quire as novel as they imply. There are various laypersons’ introductions to this, including the simple and practical magical take in julia.

There are some syntheses of these approaches that try to do everything with ODEs, all the time. [Niu, Horesh, & Chuang, 2019][#NiHC19], [Rackauckas et al., 2018][#RMDG18], and even some tutorial implementations by the indefatigable Chris Rackauckas.

I’m particularly interested on jump ODE regression

## Random stuff

My question: How can this be made Bayesian? Priors on dynamics, posterior uncertainties etc.

TBC. Lyapunov analysis, Hamiltonian dynamics.

# Refs

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Chang, Bo, Lili Meng, Eldad Haber, Lars Ruthotto, David Begert, and Elliot Holtham. 2018. “Reversible Architectures for Arbitrarily Deep Residual Neural Networks.” In. http://arxiv.org/abs/1709.03698.

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