Re:Unsupervised Feature Learning and Deep Learning Tutorial
by tfgg on Monday May 27, 2013 @07:41AM
(#5774278)
I think it's possible, it'd just take serious computing power. There are a number of physically guaranteed symmetries which would be silly to make a machine learn:
a) Permutation symmetry of identical atoms
b) Rotational symmetry of system
c) Translational symmetry of system
I suppose it could learn them approximately, given enough examples, but why bother? I think it'd be kind of like not using a convolutional neural net for recognising digits in photos and just using a bazillion more weights and examples.
I'd say you'd start with a completely general learning model which respects the above symmetries and then see where it takes it.
However, I don't know how you'd make a transferable N-body potential from a model taught only on some number of atoms. Again, I guess it's kind of like training a CNN handwriting recogniser on 256x256 images and then applying it to arbitrary sized images, which you can only do by assuming locality and translational symmetry of the features.
a) Permutation symmetry of identical atoms b) Rotational symmetry of system c) Translational symmetry of system
I suppose it could learn them approximately, given enough examples, but why bother? I think it'd be kind of like not using a convolutional neural net for recognising digits in photos and just using a bazillion more weights and examples.
I'd say you'd start with a completely general learning model which respects the above symmetries and then see where it takes it.
However, I don't know how you'd make a transferable N-body potential from a model taught only on some number of atoms. Again, I guess it's kind of like training a CNN handwriting recogniser on 256x256 images and then applying it to arbitrary sized images, which you can only do by assuming locality and translational symmetry of the features.