Hey! My dissertation is online now!
Optimizing Multiple Loss Functions with Loss-Conditional Training
- The idea behind our approach is to train a single model that covers all choices of coefficients of the loss terms, instead of training a model for each set of coefficients. We achieve this by (i) training the model on a distribution of losses instead of a single loss function, and (ii) conditioning the model outputs on the vector of coefficients of the loss terms. This way, at inference time the conditioning vector can be varied, allowing us to traverse the space of models corresponding to loss functions with different coefficients
- Applied to get on the OpenAI API waitlist
- Started figuring out igraph. Welp, it doesn’t plot because cannot load library ‘libcairo-2.dll’: error 0x7e Diesn’t seem to be a good fix. It’s a shame, because igraph seems to be great for analyzing graphs mathematically. Removing everything
- Looks like I can use networkx combined with networkx_viewer (pypi)(github). Look into that next. Upgraded from 2.1 to 2.4
- Pulled my NetworkxGraphing.py class over from Antibubbles and verified that it still works!
- Send Jason my download code
- Work on GVSETS paper
- Added formatting changes and moved footnotes to citations
- Adding a figure for the pipeline. Hmmm. It’s um… big