This semester, I took CS 294-131, a Deep Learning “special topics” course which has been offered each semester since Fall 2016 for a variable amount of class units and will be taught again next semester (the course website is already up). As usual, it was co-taught by the Trevor Darrell and Dawn Song team. The course is low-commitment for them because it’s a seminar and they don’t have to give lectures or prepare assignments and exams. CS 294-131 meets only once a week; for us, it was Mondays from 1:00PM to 2:30PM. Each meeting featured a guest speaker from academia or industry who gave a talk on his or her cutting-edge Deep Learning research results.

Here were some of the highlights for me:

  • Vladlen Koltun’s talk about his ICLR 2017 paper Learning to Act by Predicting the Future. I enjoyed his presentation, though admittedly most of it was because he was funny and actively engaging with the audience. I previously blogged about the more technical aspects here.

  • Barret Zoph and Quoc Le’s joint talk on neural architecture search, also from ICLR 2017 (here’s the OpenReview link) and also (like Koltun’s paper) an oral presentation at that conference. I’ve been hoping to find some time to read their paper and perhaps the winter break will afford me that opportunity. Zoph and Le’s presentation featured a lot of aggressive questioning from students, to the point where Professor Song asked the students to quiet down and let the speakers proceed. Fortunately, at least to me, the technical content of the presentation was interesting enough to keep my attention.

  • Ross Girshik’s talk on computer vision and object recognition. Actually, we had a fire alarm for this one, which delayed the start class for about 15 minutes … so then we had to find a new room. Unfortunately, it took about 10 more minutes to get the projector working, and then we were told we had to leave the room at around 2:10PM. At least when Girshik was actually able to talk about computer vision, I found the historical overview to be educational.

  • Percy Liang’s presentation on fighting black boxes and adversaries in Deep Learning. This was somewhat more theoretical work but he didn’t go too much into the details. I am less familiar with his work but would like to get accustomed with it as adversarial learning is a pretty hot topic in robotics these days.

In case you’re wondering, yes I had the usual sign language interpreters for the class. Yes they were unhappy, but they tried, and we were able to agree on a few terminology-related issues beforehand. And yes, I didn’t get all the technical details from the talks. I tried to allocate two hours before class to do the background reading, but inevitably that turned into 1.5 hours … and then 1 hour … and then 30 minutes. How do people manage to do class readings ahead of time when they’re juggling four major research projects? Or do people with normal hearing just find it easy to absorb almost all the technical stuff in these talks without prior preparation?1

As I mentioned earlier, CS 294-131 can be taken for a different amount of credits. This year, we had the option of taking it for 1, 2, or 3 credits. We got one credit for doing “arXiv summaries” and “discussion leads” and two for doing a class project. I decided that those summaries and discussions would be too much of a hassle, so I took CS 294-131 for two credits. It helps that I’ve long since finished my course requirements.

While I enjoyed some of the Deep Learning talks, I do have some criticisms about CS 294-131:

  • There are too many websites/links related to the class. We have Piazza, the course website, the Google group (seriously?) and Slack channels, with one for each new week. I think this is too much information and things should be centralized in two spots at most — the course website and Piazza.

  • I’m also not a fan of the arXiv leads, which was new this semester. Students had to give 1-minute presentations on Deep Learning papers that appeared on arXiv the past week. The problem with this is that the majority of students tried to cram as may technical details in their talk as possible, rather than give the clear key insight from the paper. In addition, students often went over their allotted speaking time (gee, who would have guessed??).

  • Finally, I have no idea why class participation is worth 20% of the grading here. On Piazza, we were literally told that we would get class participation credit by simply attending the lectures. Not only does it not make sense to award students who attend lectures but don’t pay attention, it also hurts those who watch the video livestreams to reduce pressure on the lecture room, since the first lecture was “standing-room only.”

To be honest, I didn’t quite enjoy the class as much as I should have, and my project didn’t turn out as well as I would have liked. I worked on a Deep Reinforcement Learning project with three other students, and hopefully that will turn into a research paper later, but in retrospect, it’s difficult to coordinate a four-person project when everyone else has other priorities.

I don’t plan to take the Spring 2018 version of the course, but I’ll certainly keep track of the papers in the background reading. I’m excited to see who the guest speakers will be this time around …

  1. For readers of this blog who are EECS PhD students (and yes, I know you read this blog) that means I’m not-so-subtly asking you to tell me how much you can absorb from technical talks and lectures. Posting as a comment here or emailing me personally works.