i just implemented a project in elixir with LLM support and would never have considered that before. (i had never used elixir before) - So who knows maybe it will help adoption?
Do any JAX experts know if there is an equivalent to https://captum.ai/ - a model interpretability library for pytorch?
In particular i want to be able to measure feature importance on both inputs and internal layers on a sample by sample basis. This is the only thing currently holding me back from using JAX right now.
Alternatively a simle to read/understand/port implementation of DeepLIFT would work too.
I think there may be a problem with this kind of analysis - it seems to me that the "riskier" plays (2 point conversion, going for it, etc.) - are more likely attempted when coaches think they will work - not randomly. To really do a fair analysis of expectancy you would need trials where the play selection is chosen randomly. Anyone else agree with me?