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@ -20,8 +20,9 @@ inclusive event. Many graduate students and researchers from industry
travel to conferences like this, were able to attend, and make the
exchanges richer.
In this post, I will try to give my impressions on the event, and
share the most interesting events and papers I saw.
In this post, I will try to give my impressions on the event, the
speakers, and the workshops that I could attend. I will do a quick
recap of the most interesting papers I saw in a future post.
[fn:volunteer] To better organize the event, and help people navigate
the various online tools, they brought in 500(!) volunteers, waved our
@ -60,7 +61,7 @@ skimming the paper or asking questions myself.
All of these excellent ideas were implemented by an [[https://iclr.cc/virtual_2020/papers.html?filter=keywords][amazing website]],
collecting all papers in a searchable, easy-to-use interface, and even
a nice [[https://iclr.cc/virtual_2020/paper_vis.html][visualisation]] of papers as a point cloud!
including a nice [[https://iclr.cc/virtual_2020/paper_vis.html][visualisation]] of papers as a point cloud!
[fn:slideslive] The videos are streamed using [[https://library.slideslive.com/][SlidesLive]], which is a
great solution for synchronising videos and slides. It is very
@ -80,8 +81,8 @@ even avoid Zoom, because of recent privacy concerns (maybe try
Overall, there were 8 speakers (two for each day of the main
conference). They made a 40-minute presentation, and then there was a
Q&A both via the chat and via Zoom. I only saw 4 of them, but I expect
I will be watching the others in the near future.
Q&A both via the chat and via Zoom. I only saw a few of them, but I
expect I will be watching the others in the near future.
** Prof. Leslie Kaelbling, [[https://iclr.cc/virtual_2020/speaker_2.html][Doing for Our Robots What Nature Did For Us]]
@ -112,12 +113,12 @@ encourage you to watch the talk!
** Dr. Laurent Dinh, [[https://iclr.cc/virtual_2020/speaker_4.html][Invertible Models and Normalizing Flows]]
This is a talk about an area of ML research I do not know very well,
but very clearly presented. I really like the approach of teaching a
set of methods from a "historical", personal point of view. Laurent
Dinh shows us how he arrived at this topic, what he finds interesting,
in a very personal and relatable manner. This has the double advantage
of introducing us to a topic that he is passionate about, while also
This is a very clear presentation of an area of ML research I do not
know very well. I really like the approach of teaching a set of
methods from a "historical", personal point of view. Laurent Dinh
shows us how he arrived at this topic, what he finds interesting, in a
very personal and relatable manner. This has the double advantage of
introducing us to a topic that he is passionate about, while also
giving us a glimpse of a researcher's process, without hiding the
momentary disillusions and disappointments, but emphasising the great
achievements. Normalizing flows are also very interesting because it
@ -138,9 +139,9 @@ go deeper in the approaches he recommends. Yoshua Bengio is also very
good at broadening the debate around deep learning, and introducing
very important concepts from cognitive science.
** Prof. Michael I. Jordan, [[https://iclr.cc/virtual_2020/speaker_8.html][The Decision-Making Side of Machine Learning: Dynamical, Statistical and Economic Perspectives]]
# ** Prof. Michael I. Jordan, [[https://iclr.cc/virtual_2020/speaker_8.html][The Decision-Making Side of Machine Learning: Dynamical, Statistical and Economic Perspectives]]
TODO
# TODO
* Workshops
@ -182,7 +183,7 @@ between ML practitioners and cognitive scientists will greatly help
advance both fields. I only watched [[https://baicsworkshop.github.io/program/baics_45.html][Leslie Kaelbling's presentation]],
which echoes a lot of things from her talk at the main conference. It
complements it nicely, with more focus on intelligence, especially
/embodied/ intelligence. I think she has the rights approach to
/embodied/ intelligence. I think she has the right approach to
relationships between AI and natural science, explicitly listing the
things from her work that would be helpful to natural scientists, and
things she wish she knew about natural intelligences. It raises many
@ -192,9 +193,8 @@ understand. I felt it was very motivational!
** [[https://iclr.cc/virtual_2020/workshops_5.html][Integration of Deep Neural Models and Differential Equations]]
I didn't attend this workshop, but I think I will watch the
presentations if I can find some time. I have found the intersection
of differential equations and ML very interesting, ever since the
famous [[https://papers.nips.cc/paper/7892-neural-ordinary-differential-equations][NeurIPS best paper]] on Neural ODEs. I think that such
improvements to ML theory from other fields in mathematics would be
extremely beneficial to a better understanding of the systems we
build.
presentations if I can find the time. I have found the intersection of
differential equations and ML very interesting, ever since the famous
[[https://papers.nips.cc/paper/7892-neural-ordinary-differential-equations][NeurIPS best paper]] on Neural ODEs. I think that such improvements to
ML theory from other fields in mathematics would be extremely
beneficial to a better understanding of the systems we build.