Now I decided to make it a bit more approachable through an interactive tool that lets you play with a polynomial that learns from incremental examples you provide. I also included some background on how the method works.
Incremental learning (in contrast to batch learning) poses a unique set of problems as the learning algorithm needs to adapt with just a single new example. Compared to the state of the art, IRMA does this through minimizing what it "forgets" about past learned data while adapting to the new example. I chose polynomials as an example as it doesn't work well with the typically used gradient descent but can be learned with IRMA in a much more stable manner.
The same approach has a closed form solution for a variety of other models (that are linear in the parameters, i.e. LIP) and I'd be interested to try and apply it to more models (like neural networks) or other tasks (like classification) as well.
I'm excited about any questions or feedback!
Nice work!
Tested on Android using both Firefox and Chrome.
Might also just be that the web-server is not handling the traffic well. Some refresh's work, others fail to load the javascript.
1. a pretty good equation has been found 2. small modifications don't have much of an impact, so it stays good
The solution I was thinking of is to do more bookkeeping and eliminate wasted work like this.
In a sense, you don't really get stuck in the same was as GP / Stochastic algos because of the memoization. You always have to be trying new solutions (parse trees)
Also wanted to explore DeepQ/RL for helping to guide the decisions of what to expand and where to expand it.