A bridge between deep neural networks and probabilistic programming

The Intelligence of Information

The paper I’ve chosen to review to start the week is quite an interesting one. It is called Probabilistic Neural Programs, and it promises to be a relevant attempt at bridging the gap between the state of the art in deep neural networks and the current developments taking place in the emerging computing paradigm of Probabilistic Programming.

As the authors point out at the outset, the current state of the art in deep learning frameworks exhibits limitations or suboptimality in the trade-offs between expressivity in the computational models and the data requirements to meet those computational demands. On the other hand deep learning is a continuous approximating algorithmic setting, while the evidence is suggesting that discrete inference algorithms outperforms continuous approximations.

Probabilistic Neural Programs 

With all this in mind the researchers publish in this post a view that claims Probabilistic Programming as a paradigm that both supports the…

View original post 1,209 more words

Advertisements

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s