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deeplearncourses.htm
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</head>
<body lang=DE link=blue vlink=purple style='tab-interval:35.4pt'>
<div class="navbar">
<a name="navbartop"></a>
<table width="100%">
<tr>
<td style="background-color: #FF6363;"><div style="float:left"><a href="#group1">Kung <br>Suganthan<br>Zaki</a></div><div style="text-align: right;">Mo, 9:30-11:15<br>
Mo, 16:45-18:30<br>
Tu, 11:45-13:30</div></td>
<td style="background-color: #FFAE00;"><div style="float:left"><a href="#group2">Adalı <br>Gleyzer<br>Gschwind</a></div><div style="text-align: right">Mo, 11:45-13:30<br>
Mo, 19:00-20:45<br>
Tu, 14:30-16:15</div></td>
<td style="background-color: #7CFF7A;"><div style="float:left"><a href="#group3">Baldi<br>Ney<br>Xing</a></div><div style="text-align: right">Mo, 14:30-16:15<br>
Tu, 9:30-11:15<br>
Tu, 16:45-18:30</div></td>
<td style="background-color: #FFE213;"><div style="float:left"><a href="#group4">Buhmann<br>Suykens<br>Zhang</a></div><div style="text-align: right">Tu, 19:00-20:45<br>
We, 11:45-13:30<br>
We, 16:45-18:30</div></td>
<td style="background-color: #5D7CFD;"><div style="float:left"><a href="#group5">Metaxas<br>Principe<br>Schuller</a></div><div style="text-align: right">We, 9:30-11:15<br>
We, 14:30-16:15<br>
Th, 11:45-13:30</div></td>
<td style="background-color: #FF78E4;"><div style="float:left"><a href="#group6">Breuel<br>Sebag<br>Suzuki</a></div><div style="text-align: right">Th, 9:30-11:15<br>
Th, 16:45-18:30<br>
Fr, 11:45-13:30</div></td>
<td style="background-color: #D47A00;"><div style="float:left"><a href="#group7">Gori<br>Kim<br>Li</a></div><div style="text-align: right">Th, 14:30-16:15<br>
Fr, 9:30-11:15<br>
Fr, 16:45-18:30</div></td>
<td style="background-color: #3DA700;"><div style="float:left"><a href="#group8">Reynolds<br>Vidal<br>Yang</a></div><div style="text-align: right">Th, 19:00-20:45<br>
Fr, 14:30-16:15<br>
Fr, 19:00-20:45</div></td>
</tr>
</table>
</div>
<div class=WordSection1>
<table class=MsoTableLightShading border=1 cellspacing=0 cellpadding=0
width="100%" style='width:100.0%;border-collapse:collapse;border:none;
mso-border-top-alt:solid black 1.0pt;mso-border-top-themecolor:text1;
mso-border-bottom-alt:solid black 1.0pt;mso-border-bottom-themecolor:text1;
mso-yfti-tbllook:1696;mso-padding-alt:0cm 5.4pt 0cm 5.4pt'>
<tr style='mso-yfti-irow:-1;mso-yfti-firstrow:yes;mso-row-margin-right:.8%'>
<td width="15%" valign=top style='width:15.5%;border-top:solid black 1.0pt;
mso-border-top-themecolor:text1;border-left:none;border-bottom:solid black 1.0pt;
mso-border-bottom-themecolor:text1;border-right:none;padding:0cm 5.4pt 0cm 5.4pt'>
<p class=MsoNormal align=center style='margin:0cm;margin-bottom:.0001pt;
text-align:center;line-height:normal;mso-yfti-cnfc:5'><b><span lang=EN-US
style='color:black;mso-themecolor:text1;mso-themeshade:191;mso-ansi-language:
EN-US;mso-no-proof:yes'>Sessions<o:p></o:p></span></b></p>
</td>
<td width="27%" valign=top style='width:27.9%;border-top:solid black 1.0pt;
mso-border-top-themecolor:text1;border-left:none;border-bottom:solid black 1.0pt;
mso-border-bottom-themecolor:text1;border-right:none;padding:0cm 5.4pt 0cm 5.4pt'>
<p class=MsoNormal align=center style='margin:0cm;margin-bottom:.0001pt;
text-align:center;line-height:normal;mso-yfti-cnfc:1'><b><span lang=EN-US
style='color:black;mso-themecolor:text1;mso-themeshade:191;mso-ansi-language:
EN-US;mso-no-proof:yes'>Course 1<o:p></o:p></span></b></p>
</td>
<td width="27%" colspan=2 valign=top style='width:27.9%;border-top:solid black 1.0pt;
mso-border-top-themecolor:text1;border-left:none;border-bottom:solid black 1.0pt;
mso-border-bottom-themecolor:text1;border-right:none;padding:0cm 5.4pt 0cm 5.4pt'>
<p class=MsoNormal align=center style='margin:0cm;margin-bottom:.0001pt;
text-align:center;line-height:normal;mso-yfti-cnfc:1'><b><span lang=EN-US
style='color:black;mso-themecolor:text1;mso-themeshade:191;mso-ansi-language:
EN-US;mso-no-proof:yes'>Course 2<o:p></o:p></span></b></p>
</td>
<td width="27%" colspan=2 valign=top style='width:27.9%;border-top:solid black 1.0pt;
mso-border-top-themecolor:text1;border-left:none;border-bottom:solid black 1.0pt;
mso-border-bottom-themecolor:text1;border-right:none;padding:0cm 5.4pt 0cm 5.4pt'>
<p class=MsoNormal align=center style='margin:0cm;margin-bottom:.0001pt;
text-align:center;line-height:normal;mso-yfti-cnfc:1'><b><span lang=EN-US
style='color:black;mso-themecolor:text1;mso-themeshade:191;mso-ansi-language:
EN-US;mso-no-proof:yes'>Course 3<o:p></o:p></span></b></p>
</td>
<td style='mso-cell-special:placeholder;border:none;padding:0cm 0cm 0cm 0cm'
width="0%"><p class='MsoNormal'> </td>
</tr>
<tr style='mso-yfti-irow:0'>
<td width="15%" valign=top style='width:15.5%;border:none;mso-border-top-alt:
solid black 1.0pt;mso-border-top-themecolor:text1;background:#FF6363;
padding:0cm 5.4pt 0cm 5.4pt'>
<p class=MsoNormal style='margin-bottom:0cm;margin-bottom:.0001pt;line-height:
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text1;mso-themeshade:191;mso-ansi-language:EN-US;mso-no-proof:yes'><a name="group1"></a>Mo,
9:30-11:15<o:p></o:p></span></b></p>
<p class=MsoNormal style='margin-bottom:0cm;margin-bottom:.0001pt;line-height:
normal;mso-yfti-cnfc:4'><b><span lang=EN-US style='color:black;mso-themecolor:
text1;mso-themeshade:191;mso-ansi-language:EN-US;mso-no-proof:yes'>Mo,
16:45-18:30<o:p></o:p></span></b></p>
<p class=MsoNormal style='margin-bottom:0cm;margin-bottom:.0001pt;line-height:
normal;mso-yfti-cnfc:4'><b><span lang=EN-US style='color:black;mso-themecolor:
text1;mso-themeshade:191;mso-ansi-language:EN-US;mso-no-proof:yes'>Tu,
11:45-13:30<o:p></o:p></span></b></p>
</td>
<td width="28%" colspan=2 valign=top style='width:28.48%;border:none;
padding:0cm 5.4pt 0cm 5.4pt'>
<p class=MsoNormal style='margin-bottom:0cm;margin-bottom:.0001pt;line-height:
normal'><span lang=EN-US style='color:black;mso-themecolor:text1;mso-themeshade:
191;mso-ansi-language:EN-US;mso-no-proof:yes'>Sun-Yuan Kung, Princeton
University<o:p></o:p></span></p>
<p class=MsoNormal style='margin-bottom:0cm;margin-bottom:.0001pt;line-height:
normal'><b style='mso-bidi-font-weight:normal'><span lang=EN-US
style='color:black;mso-themecolor:text1;mso-themeshade:191;mso-ansi-language:
EN-US;mso-no-proof:yes'>A Methodical and Cost-effective Approach to
Optimization/Generalization of Deep Learning Networks<o:p></o:p></span></b></p>
<p class=MsoNormal style='margin-bottom:0cm;margin-bottom:.0001pt;line-height:
normal'><span lang=EN-US style='color:black;mso-themecolor:text1;mso-themeshade:
191;mso-ansi-language:EN-US;mso-no-proof:yes'>INTRODUCTORY<br>
Room: Grecale<o:p></o:p></span></p>
<p class=MsoNormal style='margin-bottom:0cm;margin-bottom:.0001pt;line-height:
normal'><span lang=EN-US style='color:black;mso-themecolor:text1;mso-themeshade:
191;mso-ansi-language:EN-US;mso-no-proof:yes'><o:p> </o:p></span></p>
<p class=MsoNormal style='margin-bottom:0cm;margin-bottom:.0001pt;line-height:
normal'><b><span lang=EN-US style='color:black;mso-themecolor:text1;
mso-themeshade:191;mso-ansi-language:EN-US;mso-no-proof:yes'>Summary</span></b><span
lang=EN-US style='color:black;mso-themecolor:text1;mso-themeshade:191;
mso-ansi-language:EN-US;mso-no-proof:yes'><br>
This course will start with the introduction of two basic machine learning
subsystems: Feature Engineering (e.g. CNN for Image/Speech Feature
Extraction) and Label Engineering, e.g. Multi-layer Perceptron (MLP). The
great success of DNN in broad applications of deep learning networks hinges
upon the rich nonlinear space embedded in their nonlinear hidden (neuron)
layers. However, we face two major challenges: (1) the curse of depth and (2)
the ad hoc nature of deep learning. Fortunately, many solutions have been
proposed to effectively overcome the 'vanishing gradient' problem due to the
curse of depth. In particular, we shall elaborate (a) cross-entropy (with
amplified gradients) effective to surrogate the 0-1 loss; (b) the merit of
ReLu-neurons and (c) the vital roles of bagging, mini-batch, and dropout.<br>
It is widely recognized that the ad hoc nature of deep learning renders its
success at the mercy of trial- and-errors. To combat this problem, we
advocate a methodic and cost-effective learning paradigm (MINDnet) to train
multi-layer networks. In particular, MINDnet elegantly circumvents the curse
of depth by harnessing a new notion of omni-present supervision, i.e.
teachers hidden within a sort of 'Trojan-horse' traveling along with the
forward-propagating signals from the input to hidden layers. Therefore, one
can directly harvest teacher’s information at any hidden-layer in the MLP,
i.e. , no- propagation (NP) will be required. This will lead to a new and
slender 'inheritance layer' to summarize (inherit) all the discriminant
information embedded in the previous layer. Moreover, by augmenting the
inheritance layer with additional randomized nodes and applying again
back-propagation (BP) learning, the discriminant power of the network can be
further enhanced. Finally, we have compared MINDnet with several popular
learning models on real-world datasets, including CIFAR images, MNIST,
mHealth, HAR, Yale, Olivetti, Essex datsets. Our preliminary simulation seems
to suggest some superiority by MINDnet. For example, for the CIFAR-10
dataset, 97.9%+/-0.16% (MINDnet) > 97.4% (CutNet) > 96.0% (DenseNet)
> 93.6% (ResNet). <o:p></o:p></span></p>
<p class=MsoNormal style='margin-bottom:0cm;margin-bottom:.0001pt;line-height:
normal'><b><span lang=EN-US style='color:black;mso-themecolor:text1;
mso-themeshade:191;mso-ansi-language:EN-US;mso-no-proof:yes'>Syllabus</span></b><span
lang=EN-US style='color:black;mso-themecolor:text1;mso-themeshade:191;
mso-ansi-language:EN-US;mso-no-proof:yes'><br>
Session 1:<br>
Introduction of two basic machine learning subsystems:<br>
- Feature Engineering: CNN for Image/Speech Feature Extraction<br>
- Label Engineering: multi-layer deep learning networks<br>
Introduce supplementary (SVM-based) subsystems for validation and prediction
and highlight their<br>
vital roles in optimization and generalization.<br>
Introduce an effective surrogate function (to surrogate the 0-1 loss) in the
training phase:<br>
- How/why cross-entropy offers amplified gradients.<br>
<br>
Introduce network friendly training metrics:<br>
• equivalent optimization metrics: LSE (Gauss), FDR (Fisher) and Mutual
Information (Shannon).<br>
<br>
Session 2:<br>
Derive Back-propagation (BP) Algorithm for<br>
- back-propagation of 1st-order (gradient) and 2nd-order (Hessian) functions<br>
Discuss effective remedies for tackling the vanishing gradient problem in
deep networks:<br>
- ReLu-neuron<br>
- bagging, minim-batch, and dropout<br>
Introduce MINDnet learning paradigm:<br>
- Why the acronym MIND: Monotonically INcreasing Discriminant (MIND).<br>
- A simple solution to overcome the Curse of Depth: No-propagation (NP)
learning algorithm<br>
o How to harness the teacher information “hidden” in the hidden layer?<br>
- How to use a small number of nodes (inheritance layer) to fully summarize
(inherit) all the<br>
useful information embedded in the entire previous layer?<br>
- To highlight the vital role of BP/NP hybrid learning.<br>
Session 3:<br>
Elaborate the detailed procedure to successively construct MINDnets with
gradually growing depth:<br>
<br>
• (vertical expansion)= full Inheritance with a small number of nodes<br>
• (horizontal expansion)= Inheritance Theorem + random nodes<br>
<br>
Demonstrate that the prediction accuracy indeed improves as the MINDnet grows
deeper:<br>
• Via a Synthetic dataset, we shall conduct an extensive comparative study of
various machine<br>
learning tools in the literature.<br>
- compare MINDnet with other existing networks based real-world datasets such
as CIFAR,<br>
MNIST, Yale, Olivetti, Essex, mHealth, HAR, etc.<o:p></o:p></span></p>
<p class=MsoNormal style='margin-bottom:0cm;margin-bottom:.0001pt;line-height:
normal'><b><span lang=EN-US style='color:black;mso-themecolor:text1;
mso-themeshade:191;mso-ansi-language:EN-US;mso-no-proof:yes'>References</span></b><span
lang=EN-US style='color:black;mso-themecolor:text1;mso-themeshade:191;
mso-ansi-language:EN-US;mso-no-proof:yes'><br>
1. I. Goodfellow, Y. Bengio, and A. Courville, Deep learning. MIT Press,
Cambridge, UAA, 2016.<br>
2. C.M. Bishop, Pattern Recognition and Machine Learning, Berlin: Springer.<br>
3. S.Y. Kung, Digitial Neural Networks. Prentice Hall, 1993.<br>
4. S.Y. Kung, Kernal Methods and Machine Learning, Cambridge Press, 2014.<br>
5. Zhang, C., Bengio, S., Hardt, M., Recht, B., & Vinyals, O. (2016).
Understanding deep learning requires rethinking generalization. arXiv
preprint arXiv:1611.03530.<br>
6. Koh, P. W., & Liang, P. (2017). Understanding black-box predictions
via influence functions. arXiv preprint arXiv:1703.04730. <o:p></o:p></span></p>
<p class=MsoNormal style='margin-bottom:0cm;margin-bottom:.0001pt;line-height:
normal'><b><span lang=EN-US style='color:black;mso-themecolor:text1;
mso-themeshade:191;mso-ansi-language:EN-US;mso-no-proof:yes'>Pre-requisites</span></b><span
lang=EN-US style='color:black;mso-themecolor:text1;mso-themeshade:191;
mso-ansi-language:EN-US;mso-no-proof:yes'><br>
Linear Algebra;Understanding of the design and analysis of algorithms <o:p></o:p></span></p>
<p class=MsoNormal style='margin-bottom:0cm;margin-bottom:.0001pt;line-height:
normal'><span lang=EN-US style='color:black;mso-themecolor:text1;mso-themeshade:
191;mso-ansi-language:EN-US;mso-no-proof:yes'><o:p> </o:p></span></p>
</td>
<td width="27%" colspan=2 valign=top style='width:27.52%;border:none;
padding:0cm 5.4pt 0cm 5.4pt'>
<p class=MsoNormal style='margin-bottom:0cm;margin-bottom:.0001pt;line-height:
normal'><span lang=EN-US style='color:black;mso-themecolor:text1;mso-themeshade:
191;mso-ansi-language:EN-US;mso-no-proof:yes'>Ponnuthurai N Suganthan,
Nanyang Technological University<o:p></o:p></span></p>
<p class=MsoNormal style='margin-bottom:0cm;margin-bottom:.0001pt;line-height:
normal'><b style='mso-bidi-font-weight:normal'><span lang=EN-US
style='color:black;mso-themecolor:text1;mso-themeshade:191;mso-ansi-language:
EN-US;mso-no-proof:yes'>Learning Algorithms for Classification, Forecasting
and Visual Tracking<o:p></o:p></span></b></p>
<p class=MsoNormal style='margin-bottom:0cm;margin-bottom:.0001pt;line-height:
normal'><span lang=EN-US style='color:black;mso-themecolor:text1;mso-themeshade:
191;mso-ansi-language:EN-US;mso-no-proof:yes'>INTRODUCTORY/INTERMEDIATE<br>
Room: Maestrale<o:p></o:p></span></p>
<p class=MsoNormal style='margin-bottom:0cm;margin-bottom:.0001pt;line-height:
normal'><span lang=EN-US style='color:black;mso-themecolor:text1;mso-themeshade:
191;mso-ansi-language:EN-US;mso-no-proof:yes'><o:p> </o:p></span></p>
<p class=MsoNormal style='margin-bottom:0cm;margin-bottom:.0001pt;line-height:
normal'><b><span lang=EN-US style='color:black;mso-themecolor:text1;
mso-themeshade:191;mso-ansi-language:EN-US;mso-no-proof:yes'>Summary</span></b><span
lang=EN-US style='color:black;mso-themecolor:text1;mso-themeshade:191;
mso-ansi-language:EN-US;mso-no-proof:yes'><br>
This presentation will primarily focus on learning algorithms with reduced
iterations or no iterations at all. Some of the algorithms have closed form
solutions. Some of the algorithms do not adjust the structures once
constructed. The main algorithms considered in this talk are randomized
neural networks, kernel ridge regression and random forest. These
non-iterative methods have attracted attention of researchers due to their
high performance in terms of accuracy as well as their ability to train fast
due to their non-iterative properties or closed form training solutions. For
example, the random forest deliver the top classification performance. The
presentation will also include the basic methods as well as their state of
the art realizations. These algorithms will be benchmarked using
classification, time series forecasting and visual tracking datasets. Future
research directions will also be suggested. <o:p></o:p></span></p>
<p class=MsoNormal style='margin-bottom:0cm;margin-bottom:.0001pt;line-height:
normal'><b><span lang=EN-US style='color:black;mso-themecolor:text1;
mso-themeshade:191;mso-ansi-language:EN-US;mso-no-proof:yes'>Syllabus</span></b><span
lang=EN-US style='color:black;mso-themecolor:text1;mso-themeshade:191;
mso-ansi-language:EN-US;mso-no-proof:yes'><br>
Non-iterative algorithms or algorithms with closed-form training solutions<br>
Randomization based neural networks and their variants<br>
Kernel Ridge Regression and their variants<br>
Random Forest and their variants<br>
Applications of the above methods in classification, time series and visual
tracking<br>
Benchmarking of these methods <o:p></o:p></span></p>
<p class=MsoNormal style='margin-bottom:0cm;margin-bottom:.0001pt;line-height:
normal'><b><span lang=EN-US style='color:black;mso-themecolor:text1;
mso-themeshade:191;mso-ansi-language:EN-US;mso-no-proof:yes'>References</span></b><span
lang=EN-US style='color:black;mso-themecolor:text1;mso-themeshade:191;
mso-ansi-language:EN-US;mso-no-proof:yes'><br>
(Additional References will be included in the lecture materials)<br>
X Qiu, PN Suganthan, GAJ Amaratunga, Ensemble incremental learning Random
Vector Functional Link network for short-term electric load forecasting<br>
Knowledge-Based Systems 145, 182-196, 2018.<br>
L Zhang, PN Suganthan, Benchmarking Ensemble Classifiers with Novel
Co-Trained Kernel Ridge Regression and Random Vector Functional Link Ensembles
[Research Frontier], IEEE Computational Intelligence Magazine 12 (4), 61-72,
2017.<br>
L Zhang, PN Suganthan, Visual tracking with convolutional random vector
functional link network, IEEE Transactions on Cybernetics 47 (10), 3243-3253.<br>
L Zhang, PN Suganthan, Robust visual tracking via co-trained Kernelized
correlation filters, Pattern Recognition 69, 82-93, 2017.<br>
L Zhang, PN Suganthan, A survey of randomized algorithms for training neural
networks, Information Sciences 364, 146-155, 2016.<br>
L Zhang, PN Suganthan, Oblique decision tree ensemble via multisurface
proximal support vector machine, IEEE Transactions on Cybernetics 45 (10),
2165-2176, 2015. <o:p></o:p></span></p>
<p class=MsoNormal style='margin-bottom:0cm;margin-bottom:.0001pt;line-height:
normal'><b><span lang=EN-US style='color:black;mso-themecolor:text1;
mso-themeshade:191;mso-ansi-language:EN-US;mso-no-proof:yes'>Pre-requisites</span></b><span
lang=EN-US style='color:black;mso-themecolor:text1;mso-themeshade:191;
mso-ansi-language:EN-US;mso-no-proof:yes'><br>
Basic knowledge of neural networks, pattern classification, decision trees
will be advantageous. <o:p></o:p></span></p>
<p class=MsoNormal style='margin-bottom:0cm;margin-bottom:.0001pt;line-height:
normal'><span lang=EN-US style='color:black;mso-themecolor:text1;mso-themeshade:
191;mso-ansi-language:EN-US;mso-no-proof:yes'><o:p> </o:p></span></p>
</td>
<td width="28%" colspan=2 valign=top style='width:28.5%;border:none;
padding:0cm 5.4pt 0cm 5.4pt'>
<p class=MsoNormal style='margin-bottom:0cm;margin-bottom:.0001pt;line-height:
normal'><span lang=EN-US style='color:black;mso-themecolor:text1;mso-themeshade:
191;mso-ansi-language:EN-US;mso-no-proof:yes'>Mohammed J. Zaki, Rensselaer
Polytechnic Institute<o:p></o:p></span></p>
<p class=MsoNormal style='margin-bottom:0cm;margin-bottom:.0001pt;line-height:
normal'><b style='mso-bidi-font-weight:normal'><span lang=EN-US
style='color:black;mso-themecolor:text1;mso-themeshade:191;mso-ansi-language:
EN-US;mso-no-proof:yes'>Introductory Tutorial on Regression and Deep Learning<o:p></o:p></span></b></p>
<p class=MsoNormal style='margin-bottom:0cm;margin-bottom:.0001pt;line-height:
normal'><span lang=EN-US style='color:black;mso-themecolor:text1;mso-themeshade:
191;mso-ansi-language:EN-US;mso-no-proof:yes'>INTRODUCTORY<br>
Room: Scirocco-Libeccio<o:p></o:p></span></p>
<p class=MsoNormal style='margin-bottom:0cm;margin-bottom:.0001pt;line-height:
normal'><span lang=EN-US style='color:black;mso-themecolor:text1;mso-themeshade:
191;mso-ansi-language:EN-US;mso-no-proof:yes'><o:p> </o:p></span></p>
<p class=MsoNormal style='margin-bottom:0cm;margin-bottom:.0001pt;line-height:
normal'><b><span lang=EN-US style='color:black;mso-themecolor:text1;
mso-themeshade:191;mso-ansi-language:EN-US;mso-no-proof:yes'>Summary</span></b><span
lang=EN-US style='color:black;mso-themecolor:text1;mso-themeshade:191;
mso-ansi-language:EN-US;mso-no-proof:yes'><br>