ML Tenure-Track Faculty position at University of Montreal & Mila
The Department of Computer Science and Operations Research of University of Montreal is seeking applications for a full-time tenure-track faculty position (at assistant or associate professor level), in areas related to machine learning and its applications in connected fields (e.g., healthcare, natural language processing, computer vision, robotics). This position comes with membership to Mila, one of the largest academic research group in deep learning worldwide and combining the strengths of University of Montreal, McGill University, HEC and Polytechnique in a common beautfiul location, with nearby access to many industrial research labs like FAIR, Microsoft Research, Element AI or Borealis AI.

See more information and how to apply on the Mila page :
https://mila.quebec/en/2019/12/assistant-professor-in-machine-learning-faculte-des-arts-et-des-sciences-department-of-computer-science-and-operations-research-universite-de-montreal/

Feel free to email me for informal inquiries (use "MLJOB:" in your subject line) or approach me during the NeurIPS conference next week (many Mila professors will be there -- see https://mila.quebec/en/mila/team/ ).

Best,

Simon

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Some additional notes:

* Deadline is January 6th, 2019.

* The selected candidates could be eligible for a Canadian CIFAR
AI (CCAI) Chair. More information about the program here:
https://www.cifar.ca/ai/pan-canadian-artificial-intelligence-strategy

* Montreal is home to a very active ML community, including university-led institutes such as Mila that received considerable federal funding, industry-led ML research groups (Google, Facebook, Microsoft, Samsung, Borealis, and several more), as well as a thriving ML startup community. Montreal is a historic and cosmopolitan city, home to no less than six universities, and considered one of the best cities for students. It was recently ranked by InterNations as the top city in North America for expats: https://www.internations.org/press/press-release/the-best-and-worst-cities-in-the-world-to-live-and-work-abroad-in-2020-39934 .

* In the last hiring season, we are happy to announce that UdeM has hired Aishwarya Agrawal (https://www.cc.gatech.edu/~aagrawal307/), Irina Rish ( https://sites.google.com/site/irinarish/ ) and Pierre-Luc Bacon ( https://pierrelucbacon.com/ ).

Deep Learning for Symbolic Mathematics
https://arxiv.org/abs/1912.01412v1

Deep Learning for Symbolic Mathematics

Authors show that ANN can be surprisingly good at more elaborated tasks in mathematics, such as symbolic integration and solving differential equations.

#neuralnetworks #deeplearning #mathematics #math #matlab

https://arxiv.org/abs/1912.01412

Dream to Control: Learning Behaviors by Latent Imagination
https://arxiv.org/abs/1912.01603v1

When Does Label Smoothing Help? by Geoffrey Hinton , Rafael Müller, Simon Kornblith, https://arxiv.org/abs/1906.02629

Deep learning in The Brain : https://goo.gl/oqUXqV @ArtificialIntelligenceArticles

Reinforcement Learning for Market Making in a Multi-agent Dealer Market
https://arxiv.org/abs/1911.05892

It’s a no-brainer! Deep learning for brain MR images
https://medium.com/stanford-ai-for-healthcare/its-a-no-brainer-deep-learning-for-brain-mr-images-f60116397472

We have a lot to thank neurobiologists and neuroscientists for the #deeplearning revolution. A LOT!

In my lectures about #computervision you've heard me talk about the famous cat experiment by Torsten Wiesel and David Hubel.

Here they are with Stephen Kuffler (left) at the Department of Neurobiology at Harvard Medical School which was founded in 1966.

Source: Harvard Medical School.
PS: My 10 min lectures will go a bit into detail about the experiments and what they mean.
#artificiallintelligence #aiplaybook #research

If you're interested then do read their 1962 paper on "Receptive fields, binocular interaction and functional architecture in the cat's visual cortex" https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359523/?page=1

Training Agents using Upside-Down Reinforcement Learning
Srivastava et al.: https://arxiv.org/abs/1912.02877
#MachineLearning #ArtificialIntelligence #Robotics

NeurIPS 2019 Paper Awards
Neural Information Processing Systems Conference : https://medium.com/@NeurIPSConf/neurips-2019-paper-awards-807e41d0c1e
#ArtificialIntelligence #NeurIPS #NeurIPS2019

Amazing results applying transformers to symbolic function integration and differential equations solving by Guillaume Lample and François Charton from FAIR-Paris.

Succeeds in many cases where Mathematica fails.

Paper: https://arxiv.org/abs/1912.01412

Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One
Grathwohl et al.: https://arxiv.org/abs/1912.03263
#ArtificialIntelligence #DeepLearning #MachineLearning

The good old days... 😊

BYTE Magazine, April 1985.

Special issue on Artificial Intelligence featuring articles by Marvin Minsky, Geoffrey E. Hinton, Patrick H. Winston, Carl Hewitt, Roger Schank, Dana E. Ballard, Jerome A. Feldman, Chris Brown and several others.

Dana, Jerry and Chris were all professors of mine at the University of Rochester where I was an undergraduate at the time.

I haven't reread the articles yet, but I'm really curious to see what "Brain-like networks" are all about. :-)

NeurIPS 2019 videos

https://slideslive.com/neurips/

World's first AI to create websites in minutes

https://fronty.com/