Machine Learning
How can we show that deep learning and the brain are related? Integration of Deep Learning and Neuroscience Final Post

How can we show that deep learning and the brain are related? Integration of Deep Learning and Neuroscience Final Post

As we’ve discussed in our previous posts, this paper on the integration of deep learning and neuroscience has been highly speculative. The authors have listed deep learning-inspired hypotheses about the brain and discussed how the brain may be consistent with those hypotheses. The concluding portion of the paper discusses potential experiments that could help prove...
Paper Review: An Integration of Deep Learning and Neuroscience, Part 3

Paper Review: An Integration of Deep Learning and Neuroscience, Part 3

In our previous post we talked about how the brain might optimize cost functions. Now we’ll explore how cost functions may be generated, represented, and change over time in the brain. Marblestone et al outline several ways that cost functions could be generated.  In particular, they talk about specialized circuitry for comparing the predicted output...
Paper Review: An Integration of Deep Learning and Neuroscience, Part 2

Paper Review: An Integration of Deep Learning and Neuroscience, Part 2

One of the key sticking points in discussions comparing machine learning and the brain is how the notion of “learning” differs between computational and biological systems. In section 2 of their paper, Marblestone et al. grapple with this issue in detail. For our introduction post on this paper, go here. Deep neural networks are trained...
Paper Review: An Integration of Deep Learning and Neuroscience, Part 1

Paper Review: An Integration of Deep Learning and Neuroscience, Part 1

Nicole: More and more I see that people are very concerned with the biological plausibility of neural networks. I think this comes from the fact that we as machine learners are finally achieving human-level performance on some tasks. It has renewed faith in the idea that the best way to “solve” intelligence is to copy...
Neural Nets 101

Neural Nets 101

A neural network is a computational model inspired by neurons, and the neuronal circuits observed in biological systems.  The history behind neural networks is long and storied and could be its own blog post (and of course it is already its own blog post), so we won’t get into that here.  Instead, let’s just cover...
t-SNE and t-leaves

t-SNE and t-leaves

I stumbled upon a great blog post which discusses t-SNE.  If you’re not familiar with it, t-SNE is an algorithm that take high dimensional data (like fMRI images) and projects them down to a 2D space which is easier to view.  Of course, to make that sort of projection, some information from the high dimensional...
What should an intro to machine learning course look like?

What should an intro to machine learning course look like?

As we gear up for another semester, once again the “Introduction to Machine Learning” course offered by CMU is filled to capacity, with a long waitlist to boot.  It has become overwhelmingly one of the most popular courses for graduate students at CMU – several departments even list it as a required course. Why so...

Podcast Roundup

Welcome to 2017!  For me, 2016 was the year of the podcast.  I started listening to several Machine Learning and Academia related podcasts.  Here’s a quick round-up of my favorites from the past year… Machine Learning Focused Partially derivative – This one is good if you’re new to ML, as it assumes very little background...
ML Ethics and the "Criminal Detector"

ML Ethics and the “Criminal Detector”

Confession: I roll my eyes whenever someone says the word ‘singularity.’ As awesome frightening as it would be to have computers take over the world, the fear of that potential future distracts from the more pressing ethical concerns surrounding machine learning and AI today. Edward Snowden alerted everyone in the US to the possibility that...