Tasks

• Bills – done
• Finish chores – done
• Groceries – done

SBIRs

• Kicked off the run on the adjusted UMAP. Lt’s see what happens. Blew up immediately. I need to refactor so I’m not storing things smarter. Fixed
  • Still killed the box at 160 files though
  • I think Monday I’m going to try the batch version of the code and see if I can get something reasonable
  • I should be able to just use the last UMAP model that was saved out
• Also, just for kicks, I’d like to see if a NN could be trained to do manifold mapping based on maintaining the distance between high-dimensional points in lower-dimensional spaces. The distance function (linear, log, exponential, etc.) would adjust the learning behavior. And since the data could be loaded in batches, the memory issues are better. It’s basically an autoencoder? In fact, training an autoencoder that necks down to the desired number of dimensions (e.g 2 or 3) then attempts to reconstruct the original vector could be an interesting approach too.
• Lunch with Aaron. Fun! Discussed many things