[visionlist] CVML live Web lecture series: Web lecture 'Fast Convolution Algorithms for deep learning and computer vision', 14th December 2019, 11:00 EET

[ioannakoroni at csd.auth.gr]

Dear Computer vision/machine learning, students,  engineers, scientists and
enthusiasts,

 

Artificial Intelligence and Information analysis (AIIA) Lab, AUTH is proud
to launch the live CVML Web lecture series that will cover very important
topics Computer vision/machine learning. Top scientists internationally will
deliver these lectures, aiming at providing in-depth knowledge on various
CVML topics. The 1-hour lectures will take place on Saturdays,

to avoid conflicts with other intended registrant schedules/duties: 

a) Saturdays 11:00 EET (17:00 Beijing time) and 

b) Saturdays 20:00 EET (13:00 EST, 10:00 PST for NY/LA, respectively) for
audience in the Americas. 

Each lecture will be announced at least 1 week in advance in various
relevant email lists and in the www page:
http://icarus.csd.auth.gr/cvml-web-lecture-series/

 

Lectures will consist primarily of live lecture streaming and PPT slides.
Attendees (registrants) need no special computer equipment for attending the
lecture. They will receive the lecture PDF before each lecture and will have
the ability to ask questions real-time. Audience should have basic
University-level undergraduate knowledge of any science or engineering
department (calculus, probabilities, programming, that are typical e.g., in
any ECE, CS, EE undergraduate program).  More advanced  knowledge (signals
and systems, optimization theory, machine learning) is very helpful but nor
required.

 

The first web lecture  on 'Fast Convolution Algorithms for deep learning and
computer vision' by Professor Ioannis Pitas (see attached abstract and CV), 

will take place on 14th December 2019, 13:00 EET only.

Registration can be done using the link:
http://icarus.csd.auth.gr/cvml-web-lecture-series/

 

Fast convolution algorithms for computer vision and machine learning

 

2D convolutions play an extremely important role in machine learning, as
they form the first layers of Convolutional Neural Networks (CNNs). They are
also very important for computer vision (template matching through
correlation, correlation trackers) and in image processing (image
filtering/denoising/restoration). 3D convolutions are very important for
machine learning (video analysis through CNNs) and for video
filtering/denoising/restoration. 1D convolutions are extensively used in
digital signal processing (filtering/denoising)  and analysis (also through
CNNs)

 

Therefore, 1D/2D/3D convolution algorithms are very important both for
machine learning and for signal/image/video processing and analysis. As
their computational complexity is of the order O(N^2), O(N^4) and O(N^6)
respectively their fast execution is a must. 

 

This lecture will overview linear and cyclic convolution. Then it will
present their fast execution through FFTs, resulting in algorithms having
computational complexity of the order O(Nlog2N), O(N^2log2N) for 1D and 2D
convolutions respectively. Finally, optimal Winograd 1D and 2D convolution
algorithms will be presented having theoretically minimal number of
computations. Emphasis will be on 1D convolution algorithms, as there will
be another lecture on 2D convolution algorithms soon.

 

Prof. I. Pitas was lucky to have done his PhD degree on fast convolution
algorithms more than 30 years ago. Now this topic re-emerged as key
technology for deep learning and computer vision, primarily to address fast
CNN training and inference, thanks to advances primarily in GPU programming,
but also in multicore CPU programming.

Prof. Ioannis Pitas (IEEE fellow, IEEE Distinguished Lecturer, EURASIP
fellow) received the Diploma and PhD degree in Electrical Engineering, both
from the Aristotle University of Thessaloniki, Greece. Since 1994, he has
been a Professor at the Department of Informatics of the same University. He
served as a Visiting Professor at several Universities.

His current interests are in the areas of image/video processing, machine
learning, computer vision, intelligent digital media, human centered
interfaces, affective computing, 3D imaging and biomedical imaging. He has
published over 1138 papers, contributed in 50 books in his areas of interest
and edited or (co-)authored another 11 books. He has also been member of the
program committee of many scientific conferences and workshops. In the past
he served as Associate Editor or co-Editor of 9 international journals and
General or Technical Chair of 4 international conferences. He participated
in 70 R&D projects, primarily funded by the European Union and is/was
principal investigator/researcher in 42 such projects. He has 30000+
citations to his work and h-index 81+ (Google Scholar). 

Prof. Pitas leads the big European H2020 R&D project MULTIDRONE:
https://multidrone.eu/. He is chair of the Autonomous Systems initiative
https://ieeeasi.signalprocessingsociety.org/.

 

Prof. I. Pitas: https://scholar.google.gr/citations?user=lWmGADwAAAAJ
<https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=el> &hl=el

AIIA Lab www.aiia.csd.auth.gr <http://www.aiia.csd.auth.gr> 

 

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