Blog

Learn About Our Meetup

4200+ Members

Month: May 2017

ICML accepted papers institution stats

The accepted papers at ICML have been published. ICML is a top Machine Learning conference, and one of the most relevant to Deep Learning, although NIPS has a longer DL tradition and ICLR, being more focused, has a much higher DL density.

Most mentioned institutions

I thought it would be fun to compute some stats on institutions. Armed with Jupyter Notebook and regex, we look for all of the institution mentions, add up their counts and sort. Modulo a few annoyances:

  • I manually collapse e.g. “Google”, “Google Inc.”, “Google Brain”, “Google Research” into one category, or “Stanford” and “Stanford University”.
  • I only count up one unique mention of an institution on each paper, so if a paper has 20 people from a single institution this gets collapsed to a single mention. This way we get a better understanding of which institutions are involved on each paper in the conference.

In total we get 961 institution mentions, 420 unique. The top 30 are:

#mentions institution
---------------------
44 Google
33 Microsoft
32 CMU
25 DeepMind
23 MIT
22 Berkeley
22 Stanford
16 Cambridge
16 Princeton
15 None
14 Georgia Tech
13 Oxford
11 UT Austin
10 Duke
10 Facebook
9 ETH Zurich
9 EPFL
8 Columbia
8 Harvard
8 Michigan
7 UCSD
7 IBM
7 New York
7 Peking
6 Cornell
6 Washington
6 Minnesota
5 Virginia
5 Weizmann Institute of Science
5 Microsoft / Princeton / IAS

I’m not quite sure about “None” (15) in there. It’s listed as an institution on the ICML page and I can’t tell if they have a bug or if that’s a real cool new AI institution we don’t yet know about.

Industry vs. Academia

To get an idea of how much of the research is done at industry, I took the counts for the largest industry labs (DeepMind, Google, Microsoft, Facebook, IBM, Disney, Amazon, Adobe) and divide by the total. We get 14%, but this doesn’t capture the looong tail. Looking through the tail, I think it’s fair to say that

about 20–25% of papers have an industry involvement.

or rather, approximately three quarters of all papers at ICML have come entirely out of Academia. Also, since DeepMind/Google are both Alphabet, we can put them together (giving 60 total), and see that

6.3% of ICML papers have a Google/DeepMind author.

It would be fun to run this analysis over time. Back when I started my PhD (~2011), industry research was not as prevalent. It was common to see in Graphics (e.g. Adobe / Disney / etc), but not as much in AI / Machine Learning. A lot of that has changed and from purely subjective observation, the industry involvement has increased dramatically. However, Academia is still doing really well and contributes a large fraction (~75%) of the papers.

cool!

EDIT 1: fixed an error where previously the Alphabet stat above read 10% because I incorrectly added the numbers of DM and Google, instead of properly collapsing them to a single Alphabet entity.
EDIT 2: some more discussion and numbers on r/ML thread too.

AI workshop – TensorFlow intro

Wednesday, May 31, 2017
Door access from 6:10 to 6:30pm

We introduce and explore the basics of TensorFlow (https://www.tensorflow.org/), the second generation machine learning system behind Google Brain (https://research.google.com/teams/brain/), in a presentation+workshop setting, where we’ll walk through a simple machine learning code example together with visualizations that allows us to see what is happening in real time during the training

То access the building, please arrive between 6:10 and 6:30. Huge thank you to Ryan at Workhaus (http://workhaus.bz) for sponsoring us with the space!

At 8:00 if there is space, we’ll then head out for drinks just outside, at either Jason George (http://www.thejasongeorge.ca/) or the patio at the Corner Place (http://www.thecornerplace.ca/).

Looking forward to seeing you there!

Note: For those who attended the previous meetup: this is a replay of that event with the same workshop, providing the chance to those who were waitlisted last time to attend and anyone who is interested

Pre-requisites

– A laptop with Anaconda installed (download here (https://www.continuum.io/downloads)). Once installed, create a Python 3.5 (i.e. not 3.6) environment within Ananconda Navigator.

PLEASE REGISTER AT

AI workshop – TensorFlow intro

Wednesday, May 31, 2017, 6:15 PM

Workhaus: 100 Front St. E. 4th Floor
M5A 1E1 Toronto, ON

45 manifolds Went

We introduce and explore the basics of TensorFlow (https://www.tensorflow.org/), the second generation machine learning system behind Google Brain (https://research.google.com/teams/brain/), in a presentation+workshop setting, where we’ll walk through a simple machine learning code example together with visualizations that allows us to see what is …

Check out this Meetup →

Google auto machine learning changes its own architecture

Google is researching AutoML (Auto Machine Learning) in which it has the ability to change its own architecture directly. This can self test thousands of times to improve overall performance.

From the article: “The AutoML procedure has so far been applied to image recognition and language modeling. Using AI alone, the team have observed it creating programs that are on par with state-of-the-art models designed by the world’s foremost experts on machine learning.”

#automl #ai #artificialintelligence #torontoai

Check it out

AI Workshop – TensorFlow Intro & Social

Date: 2017 05 17

Door access from 6:10 to 6:30pm

Workhaus: 100 Front St. E. 4th Floor

We introduce and explore the basics of TensorFlow, the second generation machine learning system behind Google Brain, in a presentation+workshop setting, where we’ll walk through a simple machine learning code example together.

То access the building, please arrive between 6:10 and 6:30.  Huge thank you to Ryan at Workhaus for sponsoring us with the space!

At 8:30 we’ll then head out for drinks just outside, at either Jason George or the patio at the Corner Place.

Looking forward to seeing you there!

Pre-requisites

A laptop with Anaconda installed (download here).  Once installed, create a Python 3.5 (i.e. not 3.6) environment within Ananconda Navigator.

If you want to bring some snacks, thank you!

Next Meetup

 

Days
:
Hours
:
Minutes
:
Seconds

 

Plug yourself into AI and don't miss a beat

 


Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.