January 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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AI Seminar : February 03, 2023
by Mangannavar, Rajesh Devaraddi 31 Jan '23

31 Jan '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 (Friday), 1-2 PM PST (Add to Google Calendar <https://calendar.google.com/calendar/event?action=TEMPLATE&tmeid=M2dldjQ2cG…> ). 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://engineering.oregonstate.edu/EECS/research/AI 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: January 27, 2023
by Mangannavar, Rajesh Devaraddi 26 Jan '23

26 Jan '23

Dear all, Our next AI seminar on *"Microbial "Language" Model: Using Natural Language Processing Techniques to Understand Microbiomes" *by Xiaoli Fern is scheduled to be on January 27th(Tomorrow), 1-2 PM PST. It will be followed by a 30-minute Q&A session with the graduate students. *Location: Rogers 230* *Microbial "Language" Model: Using Natural Language Processing Techniques to Understand Microbiomes* Xiaoli Fern Associate Professor Oregon State University *Abstract:* Human microbiomes and their interactions with various body systems have been linked to a wide range of diseases and lifestyle variables. To understand these links, citizen science projects such as the American Gut Project (AGP) and Human Food Project (HFP) have provided large open source datasets for microbiome investigation. In this talk, I will present our recent work that leverages such open source datasets by learning a microbial “language” model using techniques originally developed for Natural Language Processing (NLP). Our microbial “language” model is trained in a self-supervised fashion to capture the interactions among different microbial species (taxa) and the common compositional patterns in forming microbial communities, much like the language model in NLP trained to capture word interactions and the grammatical patterns in natural texts. Importantly, the learned model allows for individual bacterial species to be interpreted and represented differently in different contexts of the microbial environment and produces a representation of a sample by collectively interpreting different bacteria species in the sample and their interactions as a whole. To demonstrate the power of our model, we show that our sample representation leads to improved prediction performance compared to baseline representations consistently across multiple tasks including predicting disease states and diet patterns. We also show that the learned representation, coupled with a simple ensemble strategy, can produce highly robust models that can generalize well to microbiome data independently collected from different populations. Finally, I will present some interpretation results that help understand our model and its behavior. *Speaker Bio:* Xiaoli Fern is an associate professor of Computer Science at Oregon State University. She received her Ph.D. (2005) in computer engineering from Purdue University and her M.S. (2000) and B.S. (1997) degrees from Shanghai Jiao Tong University. Dr. Fern is broadly interested in applied machine learning and data mining, where she draws inspirations from practical challenges from real-world applications to develop new methods and new understanding for machine learning. Her current research focuses on self-supervised learning from large and complex data with application areas spanning from material characterization and design, to learning “rules of life” from microbial and metabolomics data. Her work is sponsored by NSF, DARPA, DOE and USDA. *Please watch this space for future AI Seminars :* https://engineering.oregonstate.edu/EECS/research/AI 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: January 27, 2023
by Mangannavar, Rajesh Devaraddi 24 Jan '23

24 Jan '23

Dear all, Our next AI seminar on *"Microbial "Language" Model: Using Natural Language Processing Techniques to Understand Microbiomes" *by Xiaoli Fern is scheduled to be on January 27th(Friday), 1-2 PM PST. It will be followed by a 30-minute Q&A session with the graduate students. *Location: Rogers 230* *Microbial "Language" Model: Using Natural Language Processing Techniques to Understand Microbiomes* Xiaoli Fern Associate Professor Oregon State University *Abstract:* Human microbiomes and their interactions with various body systems have been linked to a wide range of diseases and lifestyle variables. To understand these links, citizen science projects such as the American Gut Project (AGP) and Human Food Project (HFP) have provided large open source datasets for microbiome investigation. In this talk, I will present our recent work that leverages such open source datasets by learning a microbial “language” model using techniques originally developed for Natural Language Processing (NLP). Our microbial “language” model is trained in a self-supervised fashion to capture the interactions among different microbial species (taxa) and the common compositional patterns in forming microbial communities, much like the language model in NLP trained to capture word interactions and the grammatical patterns in natural texts. Importantly, the learned model allows for individual bacterial species to be interpreted and represented differently in different contexts of the microbial environment and produces a representation of a sample by collectively interpreting different bacteria species in the sample and their interactions as a whole. To demonstrate the power of our model, we show that our sample representation leads to improved prediction performance compared to baseline representations consistently across multiple tasks including predicting disease states and diet patterns. We also show that the learned representation, coupled with a simple ensemble strategy, can produce highly robust models that can generalize well to microbiome data independently collected from different populations. Finally, I will present some interpretation results that help understand our model and its behavior. *Speaker Bio:* Xiaoli Fern is an associate professor of Computer Science at Oregon State University. She received her Ph.D. (2005) in computer engineering from Purdue University and her M.S. (2000) and B.S. (1997) degrees from Shanghai Jiao Tong University. Dr. Fern is broadly interested in applied machine learning and data mining, where she draws inspirations from practical challenges from real-world applications to develop new methods and new understanding for machine learning. Her current research focuses on self-supervised learning from large and complex data with application areas spanning from material characterization and design, to learning “rules of life” from microbial and metabolomics data. Her work is sponsored by NSF, DARPA, DOE and USDA. *Please watch this space for future AI Seminars :* https://engineering.oregonstate.edu/EECS/research/AI 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: January 20, 2023
by Mangannavar, Rajesh Devaraddi 20 Jan '23

20 Jan '23

Dear all, Our next AI seminar on *"Symbols as a Lingua Franca for Supporting Human-AI Interaction For Explainable and Advisable AI Systems" *by Subbarao Kambhampati is scheduled to be on January 20th(Tomorrow), 1-2 PM PST. It will be followed by a 30-minute Q&A session with the graduate students. *Please note that the speaker will be on zoom for the event but it will be set up in Rogers 230 for everyone to attend.* *Symbols as a Lingua Franca for Supporting Human-AI Interaction For Explainable and Advisable AI Systems* Subbarao Kambhampati Professor Arizona State University *Abstract:* Despite the surprising power of many modern AI systems that often learn their own representations, there is significant discontent about their inscrutability and the attendant problems in their ability to interact with humans. While alternatives such as neuro-symbolic approaches have been proposed, there is a lack of consensus on what they are about. There are often two independent motivations (i) symbols as a lingua franca for human-AI interaction and (ii) symbols as system-produced abstractions used by the AI system in its internal reasoning. The jury is still out on whether AI systems will need to use symbols in their internal reasoning to achieve general intelligence capabilities. Whatever the answer there is, the need for (human-understandable) symbols in human-AI interaction seems quite compelling. In particular, humans would be interested in providing explicit (symbolic) knowledge and advice -- and expect machine explanations in kind. This alone requires AI systems to maintain a symbolic interface for interaction with humans. In this talk, I will motivate this point of view, and describe recent efforts in our research group along this direction. *Speaker Bio:* Subbarao Kambhampati is a professor of computer science at Arizona State University. Kambhampati studies fundamental problems in planning and decision making, motivated in particular by the challenges of human-aware AI systems. He is a fellow of Association for the Advancement of Artificial Intelligence, American Association for the Advancement of Science, and Association for Computing machinery, and was an NSF Young Investigator. He served as the president of the Association for the Advancement of Artificial Intelligence, a trustee of the International Joint Conference on Artificial Intelligence, the chair of AAAS Section T (Information, Communication and Computation), and a founding board member of Partnership on AI. Kambhampati’s research as well as his views on the progress and societal impacts of AI have been featured in multiple national and international media outlets. He can be followed on Twitter @rao2z. *Please watch this space for future AI Seminars :* https://engineering.oregonstate.edu/EECS/research/AI 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: January 20, 2023
by Mangannavar, Rajesh Devaraddi 17 Jan '23

17 Jan '23

Dear all, Our next AI seminar on *"Symbols as a Lingua Franca for Supporting Human-AI Interaction For Explainable and Advisable AI Systems" *by Subbarao Kambhampati is scheduled to be on January 20th(Friday), 1-2 PM PST. It will be followed by a 30-minute Q&A session with the graduate students. *Please note that the speaker will be on zoom for the event but it will be set up in Rogers 230 for everyone to attend.* *Symbols as a Lingua Franca for Supporting Human-AI Interaction For Explainable and Advisable AI Systems* Subbarao Kambhampati Professor Arizona State University *Abstract:* Despite the surprising power of many modern AI systems that often learn their own representations, there is significant discontent about their inscrutability and the attendant problems in their ability to interact with humans. While alternatives such as neuro-symbolic approaches have been proposed, there is a lack of consensus on what they are about. There are often two independent motivations (i) symbols as a lingua franca for human-AI interaction and (ii) symbols as system-produced abstractions used by the AI system in its internal reasoning. The jury is still out on whether AI systems will need to use symbols in their internal reasoning to achieve general intelligence capabilities. Whatever the answer there is, the need for (human-understandable) symbols in human-AI interaction seems quite compelling. In particular, humans would be interested in providing explicit (symbolic) knowledge and advice -- and expect machine explanations in kind. This alone requires AI systems to maintain a symbolic interface for interaction with humans. In this talk, I will motivate this point of view, and describe recent efforts in our research group along this direction. *Speaker Bio:* Subbarao Kambhampati is a professor of computer science at Arizona State University. Kambhampati studies fundamental problems in planning and decision making, motivated in particular by the challenges of human-aware AI systems. He is a fellow of Association for the Advancement of Artificial Intelligence, American Association for the Advancement of Science, and Association for Computing machinery, and was an NSF Young Investigator. He served as the president of the Association for the Advancement of Artificial Intelligence, a trustee of the International Joint Conference on Artificial Intelligence, the chair of AAAS Section T (Information, Communication and Computation), and a founding board member of Partnership on AI. Kambhampati’s research as well as his views on the progress and societal impacts of AI have been featured in multiple national and international media outlets. He can be followed on Twitter @rao2z. *Please watch this space for future AI Seminars :* https://engineering.oregonstate.edu/EECS/research/AI 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: January 13, 2023
by Mangannavar, Rajesh Devaraddi 12 Jan '23

12 Jan '23

Dear all, Our next AI seminar on *"Seeing outside the image: Space and time completion for video tracking and scene parsing" *by Katerina Fragkiadakiis is scheduled to be on January 13th(Tomorrow), 1-2 PM PST. It will be followed by a 30-minute Q&A session with the graduate students. *Please note that the speaker will be on zoom for the event but it will be set up in Rogers 230 (different from the usual location) for everyone to attend.* *Seeing outside the image: Space and time completion for video tracking and scene parsing* Katerina Fragkiadaki Assistant Professor Carnegie Mellon University *Abstract:* We investigate methods for computer vision architectures to self-improve in unlabelled data, by exploiting rich regularities of the natural world. As a starting point, we embrace the fact that the world is 3D, and design neural architectures that map RGB-D observations into 3D feature maps. This representation allows us to generate self-supervision objectives using other regularities: we know that two objects cannot be in the same location at once, and that multiple views can be related with geometry. We use these facts to train viewpoint-invariant 3D features (unsupervised), and yield improvements in object detection and tracking. We then discuss entity-centric architectures where entities are informed from associative retrieval or through reconstruction feedback, and show their superior generalization over models without memory or without reconstruction feedback. We then shift focus to extracting information from dynamic scenes. We propose a way to improve motion estimation itself, by revisiting the classic concept of “particle videos”. Using learned temporal priors and within-inference optimization, we can track points across occlusions, and outperform flow-based and feature-matching methods on fine-grained multi-frame correspondence tasks. *Speaker Bio:* Katerina Fragkiadaki is an Assistant Professor in the Machine Learning Department in Carnegie Mellon University. She received her Ph.D. from University of Pennsylvania and was a postdoctoral fellow in UC Berkeley and Google research after that. Her work is on learning visual representations with little supervision and on combining spatial reasoning in deep visual learning. Her group develops algorithms for mobile computer vision, learning of physics and common sense for agents that move around and interact with the world. Her work has been awarded with a best Ph.D. thesis award, an NSF CAREER award, AFOSR Young Investigator award, a DARPA Young Investigator award, Google, TRI, Amazon, UPMC and Sony faculty research awards. *Please watch this space for future AI Seminars :* https://engineering.oregonstate.edu/EECS/research/AI 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: January 13, 2023
by Mangannavar, Rajesh Devaraddi 11 Jan '23

11 Jan '23

Dear all, Our next AI seminar on *"Seeing outside the image: Space and time completion for video tracking and scene parsing" *by Katerina Fragkiadakiis scheduled to be on January 13th(Friday), 1-2 PM PST. It will be followed by a 30-minute Q&A session with the graduate students. *Please note that the speaker will be on zoom for the event but it will be set up in Rogers 230 (different from the usual location) for everyone to attend.* *Developing AI Decision Tools for Conservation* Katerina Fragkiadaki Assistant Professor Carnegie Mellon University *Abstract:* We investigate methods for computer vision architectures to self-improve in unlabelled data, by exploiting rich regularities of the natural world. As a starting point, we embrace the fact that the world is 3D, and design neural architectures that map RGB-D observations into 3D feature maps. This representation allows us to generate self-supervision objectives using other regularities: we know that two objects cannot be in the same location at once, and that multiple views can be related with geometry. We use these facts to train viewpoint-invariant 3D features (unsupervised), and yield improvements in object detection and tracking. We then discuss entity-centric architectures where entities are informed from associative retrieval or through reconstruction feedback, and show their superior generalization over models without memory or without reconstruction feedback. We then shift focus to extracting information from dynamic scenes. We propose a way to improve motion estimation itself, by revisiting the classic concept of “particle videos”. Using learned temporal priors and within-inference optimization, we can track points across occlusions, and outperform flow-based and feature-matching methods on fine-grained multi-frame correspondence tasks. *Speaker Bio:* Katerina Fragkiadaki is an Assistant Professor in the Machine Learning Department in Carnegie Mellon University. She received her Ph.D. from University of Pennsylvania and was a postdoctoral fellow in UC Berkeley and Google research after that. Her work is on learning visual representations with little supervision and on combining spatial reasoning in deep visual learning. Her group develops algorithms for mobile computer vision, learning of physics and common sense for agents that move around and interact with the world. Her work has been awarded with a best Ph.D. thesis award, an NSF CAREER award, AFOSR Young Investigator award, a DARPA Young Investigator award, Google, TRI, Amazon, UPMC and Sony faculty research awards. *Please watch this space for future AI Seminars :* https://engineering.oregonstate.edu/EECS/research/AI 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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