Comments
There's unfortunately not much to read here yet...
Best of Machine Learning
Discover the best guides, books, papers and news in Machine Learning, once per week.
Follow the full discussion on Reddit.
Hello guys, I am trying to implement this paper. They used restricted boltzmann machines to get evolution of a system. Due to nature of the problem "activation" function is not sigmoid but tanh which is later transformed into probability. Different activation function means that gradient is different, although in the paper they wrote "standard" gradient equations. When I tried to train model with standard gradient equations I couldn't get any meaningful results.I am aware that RBM minimizes KL divergence, but I couldn't figure out how to implement that (how to get probability function). After trying to find what should I put for loss function when training with eg. Adam I stumbled upon some random github code which for loss function had:
There's unfortunately not much to read here yet...
Ever having issues keeping up with everything that's going on in Machine Learning? That's where we help. We're sending out a weekly digest, highlighting the Best of Machine Learning.
Best of Machine Learning
Discover the best guides, books, papers and news in Machine Learning, once per week.
Join 800 others in curating the best parts of Machine Learning!
Just one more step. We use social logins to make sure that real humans are curating the list!
or contribute on GitHub directly.