At long last. It took forever, but for the first time, I attended the largest and most prestigious machine learning conference, Neural Information Processing Systems (NeurIPS), held in Vancouver, Canada, from December 8-14. According to the opening video, last year in Montreal — the same place that hosted ICRA 2019 — NeurIPS had over 10,000 attendees. Tickets for NeurIPS 2018 sold out in 12 minutes, so for this year, NeurIPS actually used a lottery system for people who wanted to come. (The lottery was not for those contributing to the conference, who received a set of reserved tickets.) About 15,000 entered the lottery, and the total number of attendees was somewhere between 12,500 and 13,000.
I was only there from December 11 through 14, because the first few days were for industry-only events or tutorial talks. While those might be interesting, I also had to finish up a paper submission for a medical robotics conference. I finally submitted our paper on the night of December 10, and then the next morning, I had an early flight from San Francisco to Vancouver. My FitBit reported just 3 hours and 32 minutes of sleep, admonishing me to “Put Sleep First.” I know, I apologize. In addition, I did not have a full conference paper at NeurIPS, alas; if I did, I probably would have attended more of the conference. I had a workshop paper, which is the main reason why I attended. I am still trying to get my first full NeurIPS conference paper … believe me, it is very difficult, despite what some may say. It’s additionally tricky because my work is usually better suited for robotics conferences like ICRA.
The flight from San Francisco to Vancouver is only about 2.5 hours, and Vancouver has a halfway-decent public transportation system (BART, are you paying attention?). Thus, I was able to get to the conference convention center while it was still morning. The conference also had a luggage check, which meant I didn’t have to keep dragging my suitcase with me. Thank you!
NeurIPS 2019 was organized so that December 10-12 were the “real” (for lack of a better word) conference, with presentations and poster sessions from researchers with full, accepted conference papers. The last two days, December 13 and 14, were for the workshops, which also have papers, though those do not go through as intensive a peer-review process.
By the time I was ready to explore NeurIPS, the first of two poster sessions was happening that day. The poster sessions were, well, crowded. I don’t know if it was just me, but I was bumping into people constantly and kept having to mutter “sorry” and “excuse me.” In fact, at some point, the poster sessions had to be closed to new entrants, prompting attendees to post pictures of the “Closed” sign on Twitter, musing stuff like “Oh baby, only at NeurIPS would this happen…“.
For the 1-1.5 hours that I was at each poster session, which are formally for 2 hours each but in practice lasted about 3 hours, I probably was able to talk to only 4-5 people in each session. Am I the only one who’s struggling to talk to researchers during poster sessions?
Given the difficulty of talking to presenters at the poster session, I decided to spend some time at the industry booths. It was slightly less crowded, but not that much. Here’s a picture:
You can’t see it in the above photo, but the National Security Agency (!!) had a booth in that room. I have a little connection with the NSA: they are funding my fellowship, and I used to work there. I later would meet a former collaborator of mine from the NSA, who I hadn’t seen in many years but instantly recognized when I saw that collaborator roaming around. However, I have had no connection with the NSA for a long time and know pretty much nothing about what they are doing now, so please don’t ask me for details. While I was there I also spoke with researchers from DeepMind and a few other companies. At least for DeepMind, I have a better idea of what they are doing.
I had a pre-planned lunch with a group, and then we attended Bengio’s keynote. Yes, that Bengio who also spoke at ICRA 2019. He is constantly asked to give talks. Needless to say, the large room was packed. Bengio gave a talk about “System I and System II” in Deep Learning. Once again, I felt fortunate to have digested Thinking, Fast and Slow earlier, as you can see in my 2017 book reading list. You can find the SlidesLive recording of his talk online. There was another poster session after the talk (yes, more bumping into people and apologizing) and then I got some food at a cocktail-style dinner event that evening.
The second day was similar to the first, but with two notable differences. First, I attended a town hall meeting, where NeurIPS attendees were able to voice their concerns to the conference organizers. Second, in the evening, there was a Disability in AI event, which is a newer affinity group like the Queer in AI and Black in AI groups. At those two events, I met some of the people who I had been emailing earlier to ask about and arrange closed captioning on videos and sign language interpreting services. The Disability in AI panel talked about how to make the conference more accessible to those with disabilities. The panel members spoke about their experiences with disabilities — either personal or from a friend/relative — some of which were more severe than others. There’s some delicacy needed when describing one’s disability, such as to avoid insulting others who might have a more severe form of the disability and to avoid revealing disabilities that are hidden (if that’s important, for me it’s the opposite), but I think things proceeded OK.
I used a mix of captioning and sign language interpreting services at NeurIPS. You can find videos of NeurIPS talks on SlidesLive, complete with (some) closed captioning, but it’s not the best. The interface for the captions seems pretty unusable — it strangely was better during live recordings, when the captioning was automated. Scrolling through the myriad of workshop and conference videos on SlidesLive is also annoying. This week, I plan to write some feedback to SlidesLive and the NeurIPS conference organizers offering some advice.
I requested the interpreting for specific events where I would be walking around a lot, such as in the poster sessions, and it worked pretty well considering the stifling crowds. There was also another student at the conference who brought a team of two interpreters, so on occasion we shared the services if we were in the same events or talks. The panel discussed the idea of having a permanent sign language interpreting service from NeurIPS, which would certainly make some of my conference preparation easier! One person at the Disability in AI panel noted that “this conference is so large that we actually have two people using sign language interpreters” which is pretty much unheard of for an academic conference that doesn’t specialize in access technology or HCI more broadly.
It was nice to talk with some of the organizers, such as NeurIPS treasurer Marian Stewart Bartlett of Apple, who knew me before I had introduced myself. I also knew a little about Bartlett since she was featured in NeurIPS President Terrence Sejnowski’s Deep Learning book. Sejnowski was also briefly at the Disability in AI reception.
For the last two days of NeurIPS (December 13 and 14), we had workshops. The workshops might be the best part of NeurIPS; there are so many of them covering a wide variety of topics. This is in contrast to some other conferences I’ve attended, where workshops have been some of the least interesting or sparsely-attended portions of the conference. I don’t mean to say this negatively, it’s just my experience at various conferences. You can find the full list of workshops on the conference website, and here are the ones that seemed most interesting to me:
- Learning with Rich Experience
- Retrospectives: A Venue for Self-Reflection in ML Research
- Machine Learning for Autonomous Driving
- Bayesian Deep Learning
- Robot Learning: Control and Interaction in the Real World
- Tackling Climate Change with Machine Learning
- Fair ML in Health Care
- Deep Reinforcement Learning
I attended portions of two workshops on December 13: “Learning with Rich Experience” and “Retrospectives.” The former featured talks by Raia Hadsell of DeepMind and Pieter Abbeel of UC Berkeley. By “rich experience,” I think the workshop focuses on learning not just from images, but also videos and language. Indeed, that seems to have been featured in Hadsell and Abbeel’s talks. I would also add that John Canny has a few ongoing projects that incorporate language in the context of explainable AI for autonomous driving.
The retrospectives workshop was quite a thrill. I was there for three main reasons: (a) to understand the perspective of leaders in the ML community, (b) because many of the presenters are famous and highly accomplished, and (c) the automated captioning system would likely work better for these talks than those with more dense, technical terms. Some of the talks were by:
- Emily Denton, a research scientist at Google, who has done a lot of ground-breaking work in Generative Adversarial Networks (GANs). Her talk was largely a wake-up call to the machine learning community in that we can’t ignore the societal effects of our research. For example, she called out a full conference paper at NeurIPS 2019 which performed facial reconstruction (not recognition, reconstruction) from voice.
- Zachary Lipton, a professor at CMU and well-known among the “debunking AI hype” community. I’m embarrassed that my only interaction with him is commenting on his book reading list here. I’m probably the only person in the world who engages in that kind of conversation.
- David Duvenaud, a professor at the University of Toronto whose paper on Neural Ordinary Differential Equations (ODEs) won the best paper award at NeurIPS 2018 and has racked up over 200 citations as of today. Naturally, his talk was on all the terrible things people have said about his work, including himself but also some journalists. Seriously, did a journalist really say that Duvenaud invented the concept of an ODE?!?!? They date back to the 1600s if not earlier.
Jürgen Schmidhuber also gave a talk in this workshop.
Jürgen Schmidhuber giving a talk about Predictability Minimization and Generative Adversarial Networks at the "Retrospectives in Machine Learning" workshop. Sorry for the terrible quality of the photo above. I tried to do a panorama which failed badly, and I don't have another photo.
I don’t know why this workshop was assigned to be in a such a small room; I’m sitting in the back row in that photo. I think those who got actual chairs to sit on were in the minority. A few minutes after I took the photo above, Yoshua Bengio came and sat in front of me on the table, next to my iPad which was spitting out the SlidesLive captions. If Bengio was fuming when Schmidhuber dismissed GANs as a “simple application” of his 90s-era idea, he didn’t show it, and politely applauded with the rest of us after Schmidhuber’s talk.
In case you are new to this history, please see this NYTimes article and this Quora post for some context on the “Schmidhuber vs Hinton/LeCun/Bengio/Goodfellow” situation regarding GANs and other machine learning concepts, particularly because GANs are mentioned as one of Bengio’s technical contributions in his Turing Award citation.
Sometime in the middle of the workshop, there was a panel where Bengio, along with a few other researchers, talked about steps that could be done to improve the overall process of how research and science gets done today. Some of the topics that came up were: removing best paper awards, eliminating paper reviews (!!), and understanding how to reduce stress for younger researchers. It was refreshing to see Bengio talk about the latter topic about the pressure graduate students face, and Bengio also acknowledged that paper citations can be problematic. To put this in perspective, Bengio had the most Google Scholar citations in all of 2018, among all computer scientists, and I’m sure he was also the most cited across any field. As of today (December 22, 2019) Google Scholar shows that Bengio has 62,293 citations in 2018 and then 73,947 in 2019. Within 10 years, I would not be surprised if he is the most cited person of all time. There are a few online rankings of the most cited scholars, but most are a few years old and need updating. Joelle Pineau of McGill University brought up some good points in that while we may have high stress in our field, we are still far more fortunate than many other groups of people today, prompting applause.
Finally on the last day of the conference, the Deep Reinforcement Learning (DeepRL) workshop happened. This was one of the most, if not the most, popular NeurIPS workshop. It featured more than 100 papers, and unlike most workshop papers which are 2-4 pages, the DeepRL papers were full 8-page length papers, like normal conference papers. The workshop has a program committee size rivaling that of many full conferences! The highlights of the DeepRL workshop included, of course, AlphaStar from DeepMind and Dota2 from OpenAI. For the latter, OpenAI finally released their monstrous 66-page paper describing the system. Additionally, OpenAI gave a presentation about their Rubik’s cube robot.
NeurIPS 2019 concluded with a closing reception. The food and drinks were great, and amounted to a full dinner. During the closing reception, while music was playing nearby, Andrew Ng in his famous blue shirt attire was politely taking pictures with people who were lining up to meet him. I was tempted to take a picture of him with my phone but decided against it — I don’t want to be that kind of person who takes pictures of famous people. For his sake, I hope Ng wasn’t standing there for the entire four-hour reception!
Overall, after my four-day NeurIPS experience, here are my thoughts about networking:
- I think I was better than usual at it. NeurIPS is so large, and Berkeley is so well-represented, that there’s a good chance I’ll see someone I know when roaming around somewhere. I usually try to approach these people if I see them alone. I spoke with people who I had not seen in many years (sometimes as high as six years!), most of who were at Berkeley at some point.
- In a handful of cases, I made an appointment to see someone “at this coffee break” or “at this poster session”. Those require lots of preparation, and are subject to last-minute cancellations. I probably could have done a better job setting pre-arranged meetings, but the paper deadline I had just before coming meant I was preoccupied with other things.
- I tried to talk to anyone who was willing to talk with me, but the quality of my conversations depended on the person. I was approached by someone who is doing an online master’s program at a different university. While we had a nice conversation, there is simply no way that I would ever be collaborating with that person in the future. In contrast, it is much easier for me to talk at length with robotics PhD students from Stanford, CMU, or MIT.
In the morning of December 15, I explored Vancouver. Given my limited time, I decided to go for a run. (Yes, what a big surprise.) I hope I can come back here next year, and do more extensive running in Stanley Park. NeurIPS 2020 will return to this same exact place. My guess is that by booking two years in a row, NeurIPS could save money.
NeurIPS 2019 did not have any extracurricular highlights like the visits to Skansen or City Hall that we had at IJCAI 2019, or like the dinner reception at ICRA 2018, but the real advantage of NeurIPS is that I think the caliber of science is higher compared to other conferences.
The convention center seemed fine. However, I didn’t see a lot of extra space, so I don’t know how much more NeurIPS can absorb when it returns to Vancouver in 2020.
Remember how I wanted to come back to Sydney? NeurIPS 2021 is going to be held there, so perhaps I can return to Sydney. Additionally, according to some discussion at the town hall meeting mentioned earlier, NeurIPS will be held in New Orleans in 2022 and 2023, and then it will be in San Diego in 2024. I am wondering if anyone knows how to find statistics on the sizes and capacities of convention centers? A cursory search online didn’t yield easily digestible numbers.
In terms of “trends,” there are too many to list. I’m not going to go through a detailed list of trends, or summaries of the most interesting papers that I have seen, because I will do that in future blog posts. Here are higher-level trends and observations:
- Deep reinforcement learning remains hugely popular, though still highly concentrated within institutions such as Google, DeepMind, OpenAI, Stanford, and Berkeley.
- Meta-learning remains popular and is fast-growing.
- Fairness and privacy are fast-growing and becoming extremely popular, especially with (a) reducing societal biases of machine learning systems, and (b) health care in all aspects. In addition, it is no longer an excuse to say “we are just scientists” or “we were not aware of machine learning’s unintended consequences”. This must be part of the conversation from the beginning.
- Climate change is another fast-growing topic, though here I don’t know what the trend is like, since I don’t read papers about climate change and machine learning. I didn’t attend the climate change workshop since it conflicted with the DeepRL workshop, but I hope there was least some work that combines machine learning with nuclear energy. Nuclear energy is one of the most critical and readily usable “carbon-free” technologies we have available.
- Industry investment in machine learning continues to be strong. No signs of an “AI Winter” to me … yet.
- Diversity and inclusion, transparency, and fairness are critical. To get some insights, I encourage you to read the NeurIPS medium blog posts.
It’s great to see all this activity. I’m also enjoying reading other people’s perspectives on NeurIPS 2019, such as those from Chip Huyen. Let me know if I’m missing any interesting blog posts!
You can find some of the pictures I took at NeurIPS in my NeurIPS 2019 Flickr album. They are arranged in roughly chronological order. In the meantime, there are still several other NeurIPS-related topics that I hope to discuss. Please stay tuned for some follow-up posts.
As always, thanks for reading!