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</o:shapelayout></xml><![endif]--></head><body lang=EN-US link="#0563C1" vlink="#954F72"><div class=WordSection1><p class=MsoNormal><span style='font-size:12.0pt;line-height:105%;font-family:"Times New Roman",serif'>Dear Computer vision/machine learning, students, engineers, scientists and enthusiasts,<o:p></o:p></span></p><p class=MsoNoSpacing><o:p> </o:p></p><p class=MsoNoSpacing>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,<o:p></o:p></p><p class=MsoNoSpacing>to avoid conflicts with other intended registrant schedules/duties: <o:p></o:p></p><p class=MsoNoSpacing>a) Saturdays 11:00 EET (17:00 Beijing time) and <o:p></o:p></p><p class=MsoNoSpacing>b) Saturdays 20:00 EET (<em><span style='font-style:normal'>13:00</span> </em><span class=st>EST, 10:00 PST for NY/LA, respectively</span>) for audience in the Americas. <o:p></o:p></p><p class=MsoNoSpacing>Each lecture will be announced at least 1 week in advance in various relevant email lists and in the www page: <a href="http://icarus.csd.auth.gr/cvml-web-lecture-series/">http://icarus.csd.auth.gr/cvml-web-lecture-series/</a><o:p></o:p></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif'><o:p> </o:p></span></p><p class=MsoNoSpacing>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.<o:p></o:p></p><p class=MsoNoSpacing><o:p> </o:p></p><p class=MsoNoSpacing>The first web lecture on 'Fast Convolution Algorithms for deep learning and computer vision' by Professor Ioannis Pitas (see attached abstract and CV), <o:p></o:p></p><p class=MsoNoSpacing>will take place on 14th December 2019, 13:00 EET only.<o:p></o:p></p><p class=MsoNoSpacing>Registration can be done using the link: <a href="http://icarus.csd.auth.gr/cvml-web-lecture-series/">http://icarus.csd.auth.gr/cvml-web-lecture-series/</a><o:p></o:p></p><p class=MsoNoSpacing><o:p> </o:p></p><p class=MsoPlainText><span style='font-size:12.0pt;font-family:"Times New Roman",serif'>Fast convolution algorithms for computer vision and machine learning<o:p></o:p></span></p><p class=MsoPlainText><span style='font-size:12.0pt;font-family:"Times New Roman",serif'><o:p> </o:p></span></p><p class=MsoPlainText><span style='font-size:12.0pt;font-family:"Times New Roman",serif'>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)<o:p></o:p></span></p><p class=MsoPlainText><span style='font-size:12.0pt;font-family:"Times New Roman",serif'><o:p> </o:p></span></p><p class=MsoPlainText><span style='font-size:12.0pt;font-family:"Times New Roman",serif'>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. <o:p></o:p></span></p><p class=MsoPlainText><span style='font-size:12.0pt;font-family:"Times New Roman",serif'><o:p> </o:p></span></p><p class=MsoPlainText><span style='font-size:12.0pt;font-family:"Times New Roman",serif'>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.<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;line-height:105%;font-family:"Times New Roman",serif'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;line-height:105%;font-family:"Times New Roman",serif'>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.<o:p></o:p></span></p><p class=MsoPlainText><span style='font-size:12.0pt;font-family:"Times New Roman",serif'>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.<o:p></o:p></span></p><p class=MsoPlainText><span style='font-size:12.0pt;font-family:"Times New Roman",serif'>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). <o:p></o:p></span></p><p class=MsoPlainText><span style='font-size:12.0pt;font-family:"Times New Roman",serif'>Prof. Pitas leads the big European H2020 R&D project MULTIDRONE: <a href="https://multidrone.eu/">https://multidrone.eu/</a>. He is chair of the Autonomous Systems initiative <a href="https://ieeeasi.signalprocessingsociety.org/">https://ieeeasi.signalprocessingsociety.org/</a>.<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;line-height:105%;font-family:"Times New Roman",serif'><o:p> </o:p></span></p><p class=MsoNoSpacing>Prof. I. Pitas: <span class=MsoHyperlink><a href="https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=el">https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=el</a></span><o:p></o:p></p><p class=MsoNoSpacing>AIIA Lab <span class=MsoHyperlink><a href="http://www.aiia.csd.auth.gr">www.aiia.csd.auth.gr</a></span><o:p></o:p></p><p class=MsoNormal><span style='font-size:12.0pt;line-height:105%;font-family:"Times New Roman",serif'><o:p> </o:p></span></p></div></body></html>