Learning to rank is a key problem in information retrieval and machine learning and a core part of modern search engines and recommender systems. Off-policy learning to rank aims to optimize a ranker from implicit user feedback (e.g., clicks) collected by a deployed logging policy. However, existing off-policy learning to rank methods often make strong assumptions about how users generate the click data, i.e., the click model, and hence need to tailor their methods specifically under different click models. In this talk, I will introduce how to unify the ranking process under stochastic click models as a Markov Decision Process, and then discuss our work of leveraging offline reinforcement learning methods for click model-agnostic off-policy learning to rank.

12:30 Chair Makes Announcements and Introduces the Speaker

12:35-1:15 Speaker gives presentation

1:15-1:30 Q&A by the speaker, and final comments by the Chair

-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-

Attached is the current meeting invite and vTools link as well

-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-

IEEE Oregon Section invites you to join this WebEx meeting.

Oregon Section PES Chapter Monthly meeting

Occurs the third Tuesday of every month effective Tuesday, September 20, 2022 until Tuesday, June 20, 2023 from 12:30 PM to 1:30 PM, (UTC-07:00) Pacific Time (US & Canada)

12:30 PM  |  (UTC-07:00) Pacific Time (US & Canada)  |  1 hr

Join WebEx meeting

https://ieeemeetings.webex.com/ieeemeetings-en/j.php?MTID=m87abab220195935bdc6b261b9f6f2bfa

Meeting number:

2535 102 0973

Meeting password:

eYHPexAX536

Join from a video system or application

Dial 25351020973@ieeemeetings.webex.com

You can also dial 173.243.2.68 and enter your meeting number.

To dial from an IEEE Video Conference System: *1 2535 102 0973

Tap to join from a mobile device (attendees only)

1-855-282-6330,,25351020973## United States Toll Free

+1-415-655-0002,,25351020973## United States Toll

Join by phone

1-855-282-6330 United States Toll Free

+1-415-655-0002 United States Toll

Global call-in numbers  |  Toll-free calling restrictions

Add this meeting to your calendar. (Cannot add from mobile devices.)

Can't join the meeting? Contact support.

IMPORTANT NOTICE: Please note that this Webex service allows audio and other information sent during the session to be recorded, which may be discoverable in a legal matter. By joining this session, you automatically consent to such recordings. If you do not consent to being recorded, discuss your concerns with the host or do not join the session.

IEEE SSCS Oregon Chapter November Meeting and Seminar

Join us for a (virtual) talk from SSCS Distinguished Dr. Firooz Aflatouni from the University of Pennsylvania, Philadelphia, PA, on Friday, November 17th. The seminar will be held from 9:00am to 10:30am (PST) via a Virtual format. Please register for the meeting link and information.

Register via the included Zoom link. You need to register via Zoom (not vTools) to be able to join the meeting.

Topic:

Integrated photonic deep networks for image classification

Abstract:

The typical hardware platform for neural networks operates based on clocked computation and consists of advanced parallel graphics processing units (GPU) and/or application specific integrated circuits (ASIC), which are reconfigurable, multi-purpose and robust. However, for such platforms the input data often needs to be converted to electrical domain, digitized, and stored. Furthermore, a clocked computation system typically has a high power consumption, suffers from a limited speed, and requires a large data storage device. To address the ever-increasing demand for more sophisticated and complex AI based systems, deeper neural networks with a large number of layers and neurons are required, which result in even higher power consumption and longer computation time. Photonic deep networks could address some of these challenges by utilizing the large bandwidth available around the optical carrier and low propagation loss of CMOS-compatible photonic devices and blocks. In this talk, a low-cost integrated highly-scalable photonic architecture for implementation of deep neural networks for image/video/signal classification is presented, where the input images are taken using an array of pixels and directly processed in the optical domain. The implemented system performs computation by propagation and, as such, is several orders-of-magnitude faster than state-of-the-art clocked based systems and operates at a significantly lower power consumption. This system, which is scalable to a network with a large number of layers, performs in-domain processing (i.e. processing in the optical domain) and as a result, opto-electronic conversion, analog-to-digital conversion, and requirement for a large memory module are eliminated.

Speaker Biography:

Firooz Aflatouni (Senior Member, IEEE) received the Ph.D. degree in electrical engineering from the University of Southern California, Los Angeles, CA, USA, in 2011. In 1999, he co-founded Pardis Bargh Company, where he served as the CTO for five years working on the design and manufacturing of inclined-orbit satellite tracking systems. From 2004 to 2006, he was a Design Engineer with MediaWorks Integrated Circuits Inc., Irvine, CA. He was a Post-Doctoral Scholar with the Department of Electrical Engineering, California Institute of Technology, Pasadena, CA. He joined the University of Pennsylvania, Philadelphia, PA, USA, in 2014, where he is currently an Associate Professor with the Department of Electrical and Systems Engineering. His research interests include electronic–photonic co-design and low-power RF and millimeter-wave integrated circuits. Dr. Aflatouni received the 2020 Bell Labs Prize, the Young Investigator Program (YIP) Award from the Office of Naval Research in 2019, the NASA Early Stage Innovation Award in 2019, and the 2015 IEEE Benjamin Franklin Key Award. He is a Distinguished Lecturer of the Solid-State Circuit Society and has served on several IEEE program committees (ISSCC, CICC, and IMS). He is an Associate Editor of the IEEE Open Journal of the Solid-State Circuits Society and currently serves as the chair of IEEE Solid State Circuits Society (SSCS) Philadelphia chapter.

Oregon Section November Excom

WiFi standards have had a profound impact on how we connect and communicate in the digital age. They have transformed our daily lives, enabling us to access information, connect our devices, and engage with online services seamlessly. As WiFi standards continue to evolve, we can anticipate even faster, more reliable, and secure wireless networking experiences in the future.

Wi-Fi 7, the latest version of the ubiquitous wireless networking technology based on the IEEE 802.11 standard, is now coming to market.  This talk describes the steps that Wi-Fi has taken to get where it is today.  It includes history of the standards, regulations, integrated circuits and features that make fast, short range wireless networks possible.