[Lisa_seminaires] [Tea Talk] Clement Farabet (NVIDIA) Mon Oct 9, 2pm, Claire-McNicoll Z-209

Michael Noukhovitch mnoukhov at gmail.com
Lun 9 Oct 13:08:54 EDT 2017


TEA TALK in an HOUR!

On Fri, Oct 6, 2017, 13:00 Michael Noukhovitch <mnoukhov at gmail.com> wrote:

> *Update*: NVIDIA said they're raffling off a Titan Xp!
>
> *Also note:* I realize that this Monday is also thanksgiving and due to
> scheduling, I can't change the tea talk. I'm sorry to anyone whose weekend
> plans are being interrupted, that's my bad. I'll make sure not to schedule
> any other tea talks on holidays.
>
> Finally a *reminder:* if you do plan to attend, please sign up on the
> eventbrite link:
> https://www.eventbrite.com/e/mila-tea-talk-tickets-38158869205
>
> Thanks,
> Michael
>
> On Tue, Oct 3, 2017 at 2:01 PM Michael Noukhovitch <mnoukhov at gmail.com>
> wrote:
>
>>
>> Next week, we have a flurry of tea talks and it all starts with VP of AI
>> infrastructure at NVIDIA, *Clement Farabet* on *Monday October 9th *at
>> *2pm* at *Pavilion Claire McNicoll room Z-209*.
>>
>> There will be a *small social *after the event so you can talk directly
>> with NVIDIA researchers! NVIDIA has also been incredibly kind and will
>> bring *FREE FOOD* as well as hold a *RAFFLE!* I don't know what the
>> raffle is for, but will leave it your imagination ;)
>>
>> This is an awesome event, and hope everyone can make it! If you plan to
>> attend, please sign up on their eventbrite link:
>> https://www.eventbrite.com/e/mila-tea-talk-tickets-38158869205
>>
>> *KEYWORDS *Infrastructure, Self-driving Cars, Domain Transfer
>>
>> *TITLE*
>>
>> Deep Learning Research at NVIDIA
>>
>>
>>
>> *ABSTRACT*
>>
>> NVIDIA is the leading platform for Deep Learning research and invests in
>> a broad range of research projects within key industries, such as graphics
>> and self-driving cars. In this talk I will discuss a few of our projects
>> in-depth: from leveraging synthetic data to reduce our need on data;
>> ray-tracing for real-time virtual worlds; auto face animation for game
>> designers; and auto image transforms for photo editing. I will also talk
>> about how we apply this research work into a product and operate a
>> fast-moving R&D team to build autonomous vehicles.
>>
>>
>> *BIO*
>>
>> Clement Farabet is VP of AI Infrastructure at NVIDIA. His team is
>> responsible for building NVIDIA's next-generation AI platform,
>> leveraging NVIDIA's hardware to enable a broad range of new
>> applications, ranging from self-driving cars to medical imaging. Clement
>> Farabet received a Master’s Degree in Electrical Engineering with honors
>> from Institut National des Sciences Appliquées (INSA) de Lyon, France in
>> 2008. His Master’s thesis work on reconfigurable hardware for deep neural
>> networks was developed at the Courant Institute of Mathematical Sciences of
>> New York University with Professor Yann LeCun, and led to a patent. He then
>> joined Professor Yann LeCun’s laboratory in 2008, as a research scientist.
>> In 2009, he started collaborating with Yale University’s e-Lab, led by
>> Professor Eugenio Culurciello. This joint work later led to the creation of
>> TeraDeep (www.teradeep.com). In 2010, he started the PhD program at
>> Université Paris-Est, co-advised by Professors Laurent Najman and Yann
>> LeCun. His thesis focused on real-time image understanding/parsing with
>> deep convolutional networks. The main contributions of his thesis were
>> multi-scale convolutional networks and graph-based techniques for efficient
>> segmentations of class prediction maps. He graduated in 2013, and went on
>> to cofound Madbits, a company that focused on representing, understanding
>> and connecting images. Madbits was acquired by Twitter in 2014. At Twitter,
>> he cofounded Cortex, a team of software engineers, data scientists, and
>> research scientists dedicated to developing state-of-the-art machine
>> learning capabilities to refine and enable new products. He subsequently
>> lead and managed a team called Cortex Core, which focused on building a
>> high-leverage modular machine/deep learning platform to power every aspect
>> of the Twitter product (recommendation systems, search, timeline ranking,
>> etc.). This team focused on (1) developing models of text, images, video,
>> users, and (2) making these models seamlessly importable as components of
>> user-facing ML systems.
>>
>
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