On the whole, a lot of the arguments are realistic of ML in general. There is far too much focus on individual problems and the accuracy measure without enough focus on the interpretation of the accuracy, how well it will really generalize, etc.
On the whole, a lot of the arguments are realistic of ML in general. There is far too much focus on individual problems and the accuracy measure without enough focus on the interpretation of the accuracy, how well it will really generalize, etc.
Look at the big list of exotic models they tried: http://moleculenet.ai/models
For me, this omission is a negligence. It is selective laziness.
The author (me) does not have 20 PhD students, postdocs and startuppers under his hand, to do the job for Stanford. With my limited resources, it is more cost-effective to do my job against Stanford ;)
The MoleculeNet co-author gave another argument in the comments. My refutation is that you can't claim to lack time after 8*20 man-months have passed.