Experience with NLP solvers on a simple economic growth model
Growth Models In this post, we formulate and solve a relatively simple economic growth model where we want to find an optimal savings rate. ...
Machine learning with hard constraints: Neural Differential-Algebraic Equations (DAEs) as a general formalism - Stochastic Lifestyle
We recently released a new manuscript Semi-Explicit Neural DAEs: Learning Long-Horizon Dynamical Systems with Algebraic Constraints where we showed a way to develop neural networks where any arbitrary constraint function can be directly imposed throughout the evolution equation to near floating point accuracy. However, in true academic form it focuses directly on getting to the point about the architecture, but here I want to elaborate about the mathematical structures that surround the object, particularly the differential-algebraic equation (DAE), how its various formulations lead to the various architectures (such as stabilized neural ODEs), and elaborate on the other related architectures which haven’t had a paper yet but how you’d do it (and in what circumstances they would make sense).
Optimising FPL with Julia and JuMP
One of my talks for JuliaCon 2022 explored the use of JuMP to optimise a Fantasy Premier League (FPL) team. You can watch my presentation here: Optimising Fantasy Football with JuMP and this blog post is an accompaniment and extension to that talk. I’ve used FPL Review free expected points model and their tools to generate the team images, go check them out.