For the first time, RE•WORK will be bringing the increasingly popular Deep Learning Summit to Montreal, Canada, and are excited to announce the attendance of Yoshua Bengio, Yann LeCun, and Geoffrey Hinton who will be appearing on the Panel of Pioneers to share their expertise as the founders of the deep learning revolution. Not only have they recently been named as 3 of Forbes’ ‘Top 6 Thinkers in AI and Machine Learning’, but these leaders of the field are responsible for nurturing deep learning throughout the 80s, 90s and early 00s when others were unable to see its potential.
Whilst deep learning experienced a lull, Hinton, LeCun and Bengio laboured away in their own time at CIFAR, a research centre in Toronto where they fine tuned their abstract computational methods, and jokingly referred to themselves as the ‘deep learning conspiracy’.
The event will feature two tracks with both tracks running over 2 days. Track One will hone in on cutting edge science and research in deep learning, whilst Track Two will be focusing on the business applications.
|Super Early Bird tickets are available until Friday 2nd of June, and there are limited spaces left for these heavily discounted places. Don’t miss your chance to see the godfathers of deep learning appear on ‘The Panel of Pioneers’ together. Register now to hear from the leading minds in deep learning in Montreal this October 10 – 11.|
In addition to this phenomenal initial lineup of speakers, we are pleased to have received incredible support from the National Research Council Canada, as well as IVADO, the British Consulate-General Montreal, and the Tourisme Montréal.
“As part of Montreal’s AI ecosystem, IVADO is thrilled to partner with the RE•WORK Deep Learning Summit”, said Gilles Savard, CEO. “Combining entrepreneurship, technology and science to re-work the future using emerging technology, this Summit will shine light on one of the pillars of the fourth industrial revolution, deep learning. It will also bring together industry professionals and science researchers working on data-driven innovation, a shared goal with IVADO’s mission.”
Additionally, on speaking with the Canadian National Research Council’s Industrial Technology Advisor, Benoit Julien, he said that “Montreal is privileged to be the world’s largest R&D hub in Deep Learning with over 150 researchers involved in projects implicating dozens of local and international high tech companies. The National Research Council Industrial Research Assistance Program (NRC-IRAP) is proud to support the small and medium size businesses of this fast growing sector. Our involvement in bringing this leading edge conference to Montreal is another demonstration of our strong commitment to help Canadian companies leverage and achieve the full potential of artificial intelligence.”
|In addition to the Panel of Pioneers, we have several leading minds in deep learning confirmed to speak, including Roland Memisevic, Chief Scientist, Twenty Billion Neurons; Maithili Mavinkurve, Founder & COO, Sightline Innovation; Kyunghyun Cho, Assistant Professor of Computer Science and Data Science, New York University; Aaron Courville, Assistant Professor, University of Montreal as well as many more leaders in the field still to be announced.|
MEET THE PANEL OF PIONEERS
As one of the most cited Canadian computer scientists, Yoshua Bengio is (or has been) associate editor of the top journals in machine learning and neural networks as well as having authored two books and over 300 publications in deep learning, recurrent networks, probabilistic learning as well as other fields. His discussion will explore his main research ambition of understanding principles of learning that yield intelligence.
Yoshua Bengio is currently action editor for the Journal of Machine Learning Research, associate editor for the Neural Computation journal, editor for Foundations and Trends in Machine Learning, and has been associate editor for the Machine Learning Journal and the IEEE Transactions on Neural Networks.
Currently an Engineering Fellow at Google, Geoffrey Hinton manages Brain Team Toronto specialising in expanding deep learning. In addition to this, Hinton is professor in computer science at the University of Toronto. For over two decades he has been publishing papers on the use of artificial neural networks to simulate human processing of information in machines. An important figure in the deep learning community, Hinton was one of the pioneering researchers who was able to present the use of generalised backpropagation algorithm for training multi-layer neural nets.
Geoffrey Hinton has published countless articles across the field of deep learning, and they can be accessed here.
Yann LeCun has been director of research at Facebook since 2013 and has received much acclaim for his pioneering work in computer vision and machine learning. LeCun is also a founding director at NYU Centre for Data Science as well as Silver Professor at NYU on a part time basis working closely with the Data Scientists and the Courant Institute of Mathematical Science.
View Yann LeCun’s published works and contributions here.
In recent discussions, AI experts have suggested that at the pace deep learning is currently progressing, it could soon be the backbone of many tech products that we use every day, and the work of the trio is the foundation for the next frontier in AI technology.
|To hear from Bengio, Hinton and LeCun at the Montreal Deep Learning Summit this 10-11 October, register now and confirm your place. This event will be popular and tickets are limited. Contact Katie for more information at firstname.lastname@example.org.|
Interested in showcasing your startup?
The event provides the perfect opportunity to demo and showcase the latest AI technology and applications. If you know any innovative new companies working in the field, suggest them here.
Someone you’d like to hear from?
If you know of anyone in the industry who you’d like to hear present their research, you can suggest a speaker here.
RE•WORK have events scheduled up until October 2018. View the full calendar of events here.