I often need to discern what the State of the Art (SOTA) is for certain Machine Learning Applications, which I currently ask on the Fast.ai forums. For example:
- How does Deep Learning deal with missing values in time series?
- "Explanability" analysis for time-series regression in Deep Learning
I don't think these are on-topic, because the question essentially boils down to "what's out there?" which isn't very answerable and seems more suitable for a forum and/or wiki. However, I wanted to check my conclusion with the community before self-filtering.