[TriEmbed] Fwd: [TAR] [TAR Meeting Monday Feb 5th, 2018 at 7pm - NCSU EB1 1007]

Mahesh Balasubramaniam mbalasu at ncsu.edu
Mon Feb 5 09:20:19 CST 2018


Please feel free to register and attend tonight's IEEE R&A monthly session.

---------- Forwarded message ----------
From: Mahesh Balasubramaniam <mbalasu at ncsu.edu>
Date: Fri, Feb 2, 2018 at 1:24 AM
Subject: [TAR] [TAR Meeting Monday Feb 5th, 2018 at 7pm - NCSU EB1 1007]
To: trianglerobotics at yahoogroups.com
Cc: triembed at triembed.org, Triangle Linux Users Group General Discussion <
trilug at trilug.org>, humanoidrobotteam <humanoidrobotteam at googlegroups.com>

Friends,

We are having a meeting on Feb 05, 2018 at 7pm (6:20 pm for pizza) in 1007
NCSU Engineering Building 1.  The subject will be

*"**Tripping the light GANtastic: Understanding objects and scenes with
Generative Adversarial Networks – Dr. Matthew Phillips**"*

Please refer to the registration link below for more details.

registration link :

https://events.vtools.ieee.org/m/160206

Requesting all of your co-ordination to mindfully welcome, appreciate and
get inspired by Dr. Matthew Phillips for his work involving computer vision
and deep learning.

*Generative adversarial networks (GANs)* are a class of artificial
Intelligence algorithms used in unsupervised machine learning, implemented
by a system of two neural networks
<https://en.wikipedia.org/wiki/Neural_network> contesting with each other
in a zero-sum game framework.

In simple words,* GANs* are artificial neural networks that work together
to give better answers. One neural network is the tricky network, and the
other one is the useful network. The tricky network will try to give an
input to the useful network that will cause the useful network to give a
bad answer. The useful network will then learn not to give a bad answer,
and the tricky network will try to trick the useful network again. As this
continues, the useful network will get better and not become tricked as
often, and the useful network will be able to be used to make good
predictions.

*GANs* provide a way to learn deep representations without extensively
annotated training data. They achieve this through deriving backpropagation
signals through a competitive process involving a pair of networks.

*GANs* have received wide attention in the machine learning field because
of their potential to learn high-dimensional, complex real data.
Specifically, they do not perform distribution assumptions and can simply
infer real-like samples from latent space. This powerful property leads
GANs to be applied to various applications such as image synthesis, image
attribute editing, image translation, image super-resolution and
classification.

*Bio of the speaker:*

*Dr. Matthew Phillips *received a B.A. in philosophy and mathematics from
Tufts University, and he received a Ph.D. in philosophy with a certificate
in cognitive science from Rutgers University. *After he completed his
Ph.D., he moved into neuroscience, where he first focused on
visual/oculomotor psychophysics. He later shifted to primate cellular
electrophysiology and, finally, to computational neuroscience. His focus on
computational neuroscience brought him to Duke University and the Research
Triangle Park (RTP). In the RTP, Matt worked briefly as a C++ engineer and
pursued side projects in machine learning. He now works full time in
machine learning and computer vision at Kitware.*

*Dr. Phillips has received numerous awards, grants and fellowships. In
2005, he received the James S. McDonnell Foundation 21st Century
Postdoctoral Fellowship Award, which he declined. A year later, he received
a Fight for Sight fellowship. Then, in 2008, he accepted an award to
present at the Advances in Computational Motor Control meeting. In the same
year, he began a Postdoctoral Individual National Research Service Award
from the National Eye Institute of the National Institutes of Health for
“Using saccadic adaptation to probe the coordinate system of parietal
neurons.” Later, in 2012, he received a research grant from the American
Academy of Neurology for “Assessing efficiency of learning the neurologic
exam with a visual tracking device” with co-principal investigator James
Noble, MD.*

Dr. Phillips has also contributed to considerable applications development
and open source projects including signal processing algorithms, neural
waveforms and computer vision based analytics.

Please refer to the following link to know more about Dr. Phillips,

https://www.kitware.com/matthew-phillips/

IEEE ENCS RA24 chapter appreciates the passion, drive, highly impressive
efforts from Dr. Phillips and wishes the very best to his career & life.

Thank you.


Mahesh Balasubramaniam
IEEE ENCS RA24 Chair
mbalasu at ncsu.edu
919-649-6902
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