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

Michael Noukhovitch mnoukhov at gmail.com
Lun 9 Oct 13:45:07 EDT 2017


Tea Talk in 15!

See attached ;)



On Mon, Oct 9, 2017, 13:08 Michael Noukhovitch <mnoukhov at gmail.com> wrote:

> 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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