BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:US/Mountain BEGIN:DAYLIGHT DTSTART:20210314T030000 TZOFFSETFROM:-0700 TZOFFSETTO:-0600 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:MDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20211107T010000 TZOFFSETFROM:-0600 TZOFFSETTO:-0700 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:MST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20210629T014200Z UID:2B070115-F5B0-4648-AF17-F29685FAB8E7 DTSTART;TZID=US/Mountain:20210628T180000 DTEND;TZID=US/Mountain:20210628T193000 DESCRIPTION:While 5G deployment is being carried out in many places of the world\, there has been great interest in the prospects of 5G beyond and th e next generation. Among the various visions\, a common theme is that arti ficial intelligence will play a key role\, as evidenced by the great inter est and advances in machine learning enabled wireless communications and n etworking. In this talk\, we will discuss the motivation\, potential\, and challenges of incorporating machine learning in wireless communications a nd networking for 5G and beyond systems.\n\nWe will start with two motivat ing examples\, i.e.\, channel estimation and mobile edge computing\, to sh ow why machine learning could be helpful. We will share our experience of several case studies\, including (i) a hybrid approach to the classical en ergy efficiency maximization problem\, where traditional models could be u sed to train a deep learning model\; (ii) data augmentation for convolutio nal neural network (CNN) based automatic modulation classification (AMC)\, where a conditional generative adversarial network (CGAN) is utilized to generate synthesized training data\; and (iii) and an adaptive model for R FID-based 3D human skeleton tracking\, which utilizes meta-learning and fe w-shot fine-tuning to achieve high adaptability to new environments. We wi ll conclude this talk with a discussion of challenges and open problems.\n \nCo-sponsored by: Kingston CA COMSOC Chapter\n\nSpeaker(s): Dr. Shiwen Ma o\, \n\nAgenda: \nVirtual Distinguised Lecture by Dr. Shiwen Mao (Auburn U niversity)\n\n6pm (MT) - Introductions\n\n6:10-7:15 - VDL Presentation\n\n 7:15-7:30 - Q&A\n\nDenver\, Colorado\, United States\, Virtual: https://ev ents.vtools.ieee.org/m/274419 LOCATION:Denver\, Colorado\, United States\, Virtual: https://events.vtools .ieee.org/m/274419 ORGANIZER:trweil@ieee.org SEQUENCE:10 SUMMARY:Machine Learning for Wireless Communications and Networking: Motiva tions\, Case Studies\, and Open Problems URL;VALUE=URI:https://events.vtools.ieee.org/m/274419 X-ALT-DESC:Description: <br /><p>While 5G deployment is being carried out i n many places of the world\, there has been great interest in the prospect s of 5G beyond and the next generation. Among the various visions\, a comm on theme is that artificial intelligence will play a key role\, as evidenc ed by the great interest and advances in machine learning enabled wireless communications and networking. In this talk\, we will discuss the motivat ion\, potential\, and challenges of incorporating machine learning in wire less communications and networking for 5G and beyond systems.&nbsp\;</p>\n <p>We will start with two motivating examples\, i.e.\, channel estimation and mobile edge computing\, to show why machine learning could be helpful. We will share our experience of several case studies\, including (i) a hy brid approach to the classical energy efficiency maximization problem\, wh ere traditional models could be used to train a deep learning model\; (ii) data augmentation for convolutional neural network (CNN) based automatic modulation classification (AMC)\, where a conditional generative adversari al network (CGAN) is utilized to generate synthesized training data\; and (iii) and an adaptive model for RFID-based 3D human skeleton tracking\, wh ich utilizes meta-learning and few-shot fine-tuning to achieve high adapta bility to new environments. We will conclude this talk with a discussion o f challenges and open problems.&nbsp\;</p><br /><br />Agenda: <br /><p>Vir tual Distinguised Lecture by Dr. Shiwen Mao (Auburn University)</p>\n<p>6p m (MT) - Introductions</p>\n<p>6:10-7:15 - VDL Presentation</p>\n<p>7:15-7 :30 - Q&amp\;A</p> END:VEVENT END:VCALENDAR