4 ms·
A few comments have mentioned neural nets in this post. adamnemecek mentions in this thread that PGMs are a superset of neural networks, and and Thomas Wiecki
by diab0lic 10y ago
A few comments have mentioned neural nets in this post. adamnemecek mentions in this thread that PGMs are a superset of neural networks, and
and Thomas Wiecki has a few excellent blog posts on creating bayesian neural networks using pymc3.[0][1][2] If you're curious about how these two concepts can be brought together I highly recommend reading through these three posts.
[0] http://twiecki.github.io/blog/2016/06/01/bayesian-deep-learning/ http://twiecki.github.io/blog/2016/06/01/bayesian-deep-learn...
[1] http://twiecki.github.io/blog/2016/07/05/bayesian-deep-learning/ http://twiecki.github.io/blog/2016/07/05/bayesian-deep-learn...
[2] http://twiecki.github.io/blog/2017/03/14/random-walk-deep-net/ http://twiecki.github.io/blog/2017/03/14/random-walk-deep-ne...
- ganfortran 10y agoDo they train with backpropagation efficiently?
- twiecki 10y agoNo, back-propagation would not give full Bayesian inference (although there are some tricks [0]). They instead use variational inference[1], which allows for fast inference of continuous PGMs. [0] http://mlg.eng.cam.ac.uk/yarin/blog_2248.html http://mlg.eng.cam.ac.uk/yarin/blog_2248.html [1] https://arxiv.org/abs/1603.00788 https://arxiv.org/abs/1603.00788
- cttet 10y agoMost variational inference are not full-Bayesian as well...