February 2023 - Ai - Engineering Mailing Lists

information theory class
by Nguyen, Thinh 05 Jan '24

05 Jan '24

Hello everyone, I just want to bring to your attention that I will be teaching information theory class (ECE 566) next term. If you are interested in learning the theories and some foundational connections among various disciplines (compression, compression, machine learning, statistics, ...) then you might want to consider taking ECE 566. I am attaching the ECE 566 syllabus. If enough number of student register for ECE 566, I will offer the second course (special topics) in information theory series in the Spring term. Best, Dr. Nguyen

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Fwd: ECE586/AI586 Applied Matrix Analysis
by Fu, Xiao 28 Feb '23

28 Feb '23

Hi Folks, I hope to mention that our special topic course “ECE599/AI539 Matrix Analysis for Signal Processing and Machine Learning” has been converted to a regular number course “ECE586/AI586 Applied Matrix Analysis”. The course contents can be seen in the attached slides. The new regular-number course is going to be offered in Spring 2023. For those who are interested in machine learning/signal processing theory and algorithms (and their deep connections with linear algebra), please consider signing up. Best, Xiao ======================= Xiao Fu, Assistant Professor School of Electrical Engineering and Computer Science Oregon State University Corvallis OR 97331 3003 Kelley Engineering Center Homepage: https://web.engr.oregonstate.edu/~fuxia/<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fweb.engr.…>

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AI Seminar : February 24, 2023
by Mangannavar, Rajesh Devaraddi 23 Feb '23

23 Feb '23

Dear all, Our next AI seminar on *"The mathematics of neural networks: recent advances, thoughts, and the path forward" *by Mikhail Belkin is scheduled to be on February 24th (Tomorrow), 1-2 PM PST (Add to Google Calendar <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcalendar.…> ). It will be followed by a 30-minute Q&A session with the graduate students. *Location: Rogers 230* *The mathematics of neural networks: recent advances, thoughts, and the path forward* Mikhail Belkin Professor University of California, San Diego *Abstract:* The recent remarkable practical achievements of neural networks have far outpaced our theoretical understanding of their properties. Yet, it is hard to imagine that progress can continue indefinitely, without deeper understanding of their fundamental principles and limitations. In this talk I will discuss some recent advances in the mathematics of neural networks, including some of our recent work, and outline what are in my opinion are some promising directions for the future research. *Speaker Bio:* Mikhail Belkin received his Ph.D. in 2003 from the Department of Mathematics at the University of Chicago. His research interests are in theory and applications of machine learning and data analysis. Some of his well-known work includes widely used Laplacian Eigenmaps, Graph Regularization and Manifold Regularization algorithms, which brought ideas from classical differential geometry and spectral analysis to data science. His recent work has been concerned with understanding remarkable mathematical and statistical phenomena observed in deep learning. This empirical evidence necessitated revisiting some of the basic concepts in statistics and optimization. One of his key recent findings is the "double descent" risk curve that extends the textbook U-shaped bias-variance trade-off curve beyond the point of interpolation. He has served on the editorial boards of the Journal of Machine Learning Research, IEEE Pattern Analysis and Machine Intelligence and SIAM Journal on Mathematics of Data Science. *Please watch this space for future AI Seminars :* https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri… Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> For graduate students in the AI program, attendance is strongly encouraged.

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AI Seminar : February 24, 2023
by Mangannavar, Rajesh Devaraddi 20 Feb '23

20 Feb '23

Dear all, Our next AI seminar on *"The mathematics of neural networks: recent advances, thoughts, and the path forward" *by Mikhail Belkin is scheduled to be on February 24th (Friday), 1-2 PM PST (Add to Google Calendar <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcalendar.…> ). It will be followed by a 30-minute Q&A session with the graduate students. *Location: Rogers 230* *The mathematics of neural networks: recent advances, thoughts, and the path forward* Mikhail Belkin Professor University of California, San Diego *Abstract:* The recent remarkable practical achievements of neural networks have far outpaced our theoretical understanding of their properties. Yet, it is hard to imagine that progress can continue indefinitely, without deeper understanding of their fundamental principles and limitations. In this talk I will discuss some recent advances in the mathematics of neural networks, including some of our recent work, and outline what are in my opinion are some promising directions for the future research. *Speaker Bio:* Mikhail Belkin received his Ph.D. in 2003 from the Department of Mathematics at the University of Chicago. His research interests are in theory and applications of machine learning and data analysis. Some of his well-known work includes widely used Laplacian Eigenmaps, Graph Regularization and Manifold Regularization algorithms, which brought ideas from classical differential geometry and spectral analysis to data science. His recent work has been concerned with understanding remarkable mathematical and statistical phenomena observed in deep learning. This empirical evidence necessitated revisiting some of the basic concepts in statistics and optimization. One of his key recent findings is the "double descent" risk curve that extends the textbook U-shaped bias-variance trade-off curve beyond the point of interpolation. He has served on the editorial boards of the Journal of Machine Learning Research, IEEE Pattern Analysis and Machine Intelligence and SIAM Journal on Mathematics of Data Science. *Please watch this space for future AI Seminars :* https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri… Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> For graduate students in the AI program, attendance is strongly encouraged.

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AI Seminar : February 17, 2023
by Mangannavar, Rajesh Devaraddi 16 Feb '23

16 Feb '23

Dear all, Our next AI seminar on *"**The Data Pyramid for Building Generalist Agents* *" *by Yuke Zhu is scheduled to be on February 17th (Tomorrow), 1-2 PM PST (Add to Google Calendar <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcalendar.…> ). It will be followed by a 30-minute Q&A session with the graduate students. *Location: Rogers 230* *The Data Pyramid for Building Generalist Agents* Yuke Zhu Assistant Professor UT-Austin *Abstract:* Recent advances in AI and Machine Learning have made great strides in developing robust and adaptive agents in the real world. Nonetheless, unlike the recent remarkable multi-task consolidations in Natural Language Processing and Computer Vision, today’s Embodied AI research has mainly focused on building siloed systems for narrow tasks. We argue that the crux of building generalist agents is harnessing massive, diverse, and multimodal data altogether. This talk will examine various sources of training data available for training embodied agents, from Internet-scale corpora to task demonstrations. We will discuss the complementary values and limitations of these data in a pyramid structure and introduce our recent efforts in building generalist agents with this data pyramid. *Speaker Bio:* Yuke Zhu is an Assistant Professor in the Computer Science department of UT-Austin, where he directs the Robot Perception and Learning Lab. He is also a core faculty at Texas Robotics and a senior research scientist at NVIDIA. His research lies at the intersection of robotics, machine learning, and computer vision. He received his Master's and Ph.D. degrees from Stanford University. His research works have won several awards and nominations, including the Best Conference Paper Award in ICRA 2019, Outstanding Learning Paper at ICRA 2022, Outstanding Paper at NeurIPS 2022, and Best Paper Finalists in IROS 2019, 2021. He is the recipient of the NSF CAREER Award and the Amazon Research Awards. *Please watch this space for future AI Seminars :* https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri… Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> For graduate students in the AI program, attendance is strongly encouraged.

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AI Seminar : February 17, 2023
by Mangannavar, Rajesh Devaraddi 13 Feb '23

13 Feb '23

Dear all, Our next AI seminar on *"**The Data Pyramid for Building Generalist Agents* *" *by Yuke Zhu is scheduled to be on February 17th (Friday), 1-2 PM PST (Add to Google Calendar <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcalendar.…> ). It will be followed by a 30-minute Q&A session with the graduate students. *Location: Rogers 230* *The Data Pyramid for Building Generalist Agents* Yuke Zhu Assistant Professor UT-Austin *Abstract:* Recent advances in AI and Machine Learning have made great strides in developing robust and adaptive agents in the real world. Nonetheless, unlike the recent remarkable multi-task consolidations in Natural Language Processing and Computer Vision, today’s Embodied AI research has mainly focused on building siloed systems for narrow tasks. We argue that the crux of building generalist agents is harnessing massive, diverse, and multimodal data altogether. This talk will examine various sources of training data available for training embodied agents, from Internet-scale corpora to task demonstrations. We will discuss the complementary values and limitations of these data in a pyramid structure and introduce our recent efforts in building generalist agents with this data pyramid. *Speaker Bio:* Yuke Zhu is an Assistant Professor in the Computer Science department of UT-Austin, where he directs the Robot Perception and Learning Lab. He is also a core faculty at Texas Robotics and a senior research scientist at NVIDIA. His research lies at the intersection of robotics, machine learning, and computer vision. He received his Master's and Ph.D. degrees from Stanford University. His research works have won several awards and nominations, including the Best Conference Paper Award in ICRA 2019, Outstanding Learning Paper at ICRA 2022, Outstanding Paper at NeurIPS 2022, and Best Paper Finalists in IROS 2019, 2021. He is the recipient of the NSF CAREER Award and the Amazon Research Awards. *Please watch this space for future AI Seminars :* https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri… Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> For graduate students in the AI program, attendance is strongly encouraged.

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No AI seminar this week
by Tadepalli, Prasad 09 Feb '23

09 Feb '23

Dear all, There is no AI seminar this week, but there are two external speakers scheduled for next two weeks. Please see here for more details: https://engineering.oregonstate.edu/EECS/research/AI-seminars<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…> In addition, we also expect to have multiple faculty candidates who will be giving zoom seminars at different times. Please stay tuned and try to attend as many talks as you can. Thank you, Prasad

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AI Seminar : February 03, 2023
by Mangannavar, Rajesh Devaraddi 02 Feb '23

02 Feb '23

Dear all, Our next AI seminar on *"AI can learn from data. But can it learn to reason?" *by Guy Van den Broeck is scheduled to be on February 3rd (Tomorrow), 1-2 PM PST (Add to Google Calendar <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcalendar.…> ). It will be followed by a 30-minute Q&A session with the graduate students. *Location: Rogers 230* *AI can learn from data. But can it learn to reason?* Guy Van den Broeck University of California at Los Angeles *Abstract:* Modern artificial intelligence, and deep learning in particular, is extremely capable at learning predictive models from vast amounts of data. Many expect that AI will go from powering customer service chatbots to providing mental health services. That it will go from personalized advertisement to deciding who is given bail. The expectation is that AI will solve society’s problems by simply being more intelligent than we are. Implicit in this bullish perspective is the assumption that AI will naturally learn to reason from data: that it can form trains of thought that “make sense,” similar to how a mental health professional, a judge, or a lawyer might reason about a case, or more formally, how a mathematician might prove a theorem. This talk will investigate the question whether this behavior can be learned from data, and how we can design the next generation of artificial intelligence techniques that can achieve such capabilities, focusing on neuro-symbolic learning and tractable deep generative models. *Speaker Bio:* Guy Van den Broeck is an Associate Professor and Samueli Fellow at UCLA, in the Computer Science Department, where he directs the Statistical and Relational Artificial Intelligence (StarAI) lab. His research interests are in Machine Learning, Knowledge Representation and Reasoning, and Artificial Intelligence in general. His papers have been recognized with awards from key conferences such as AAAI, UAI, KR, OOPSLA, and ILP. Guy is the recipient of an NSF CAREER award, a Sloan Fellowship, and the IJCAI-19 Computers and Thought Award. *Please watch this space for future AI Seminars :* https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri… Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> For graduate students in the AI program, attendance is strongly encouraged.

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