October 2021 - Ai - Engineering Mailing Lists

AI seminar: Nov 3 2021
by Mangannavar, Rajesh Devaraddi 29 Oct '21

29 Oct '21

Dear all, Our next AI seminar on *"Modularity and Compositionality in Multi-Step Robot Manipulation" *by Caelan Garrett is scheduled to be on November 3rd, 1-2 PM PST. It will be followed by a 30 minute Q&A session by the graduate students. Note that this is a zoom event. Zoom Link: https://oregonstate.zoom.us/j/93591935144?pwd=YjZaSjBYS0NmNUtjQzBEdzhPeDZ5U… *Modularity and Compositionality in Multi-Step Robot Manipulation* Caelan Garrett Research Scientist NVIDIA *Abstract:*We seek to program a robot to autonomously complete complex tasks in a variety of real-world settings involving different environments, objects, manipulation skills, degrees of observability, initial states, and goal objectives. In order to successfully generalize across these settings, we take a model-based approach to building the robot's policy, which enables it to reason about the effects of it executing different sequences of parameterized manipulation skills. Specifically, we introduce a general-purpose hybrid planning framework that uses streams, modules that encode sampling procedures, to generate continuous parameter-value candidates. We present several domain-independent algorithms that efficiently combine streams in order to solve for parameter values that jointly satisfy the constraints necessary for a sequence of skills to achieve the goal. Each stream can be either engineered to perform a standard robotics subroutine, like inverse kinematics and collision checking, or learned from data to capture difficult-to-model behaviours, such as pouring, scooping, and grasping. Streams are also able to represent probabilistic inference operations, which enables our framework to plan in belief space and intentionally select actions that reduce the robot's uncertainty about the unknown world. We demonstrate the generality of our approach by applying it to several real-world tabletop, kitchen, and construction tasks and show that it can even be effective in settings involving objects that the robot has never seen before. *Speaker Bio:* Caelan Garrett is a research scientist at NVIDIA's Seattle Robotics Lab which is led by Professor Dieter Fox. He received his PhD at MIT in the Learning and Intelligent Systems group within CSAIL where he was advised by Professors Tomás Lozano-Pérez and Leslie Pack Kaelbling. His research is on integrating robot motion planning, discrete AI planning, and machine learning to flexibly and efficiently plan for autonomous mobile manipulators operating in human environments. He recently authored the first survey paper on integrated task and motion planning. He is a recipient of the NSF Graduate Research Fellowship. He has previously interned in the autonomous vehicle industry while at Optimus Ride and in the autonomous fulfilment industry while at Amazon Robotics. This talk will be followed by a talk on the topic "*Estimating Long-term Rewards by Off-policy Reinforcement Learning*" by Lihong Li on 10th November 2021. *Please watch this space for future AI Seminars: * * https://eecs.oregonstate.edu/ai-events <https://eecs.oregonstate.edu/ai-events>* Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> The AI Seminar has a strict "no electronics" and "no recordings" policy. -> For graduate students in the AI program, attendance is strongly encouraged.

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[CoRIS-List] Jay Patravali -- Masters Defense: Oct 26th, 12PM Pacific | KEC 1007 and Virtual Zoom Meeting
by Patravali, Jay Sanjay 25 Oct '21

25 Oct '21

Dear all, I'll be defending my thesis on Tuesday 26th October at 12:00 PM in KEC 1007 and virtually through zoom. Feel free to join either way if you're interested! *Title:* Annotation-Efficient Video Representation Learning *Date & Time:* Tuesday, October 26th at 12:00 pm *Place:* KEC1007 and Zoom Meeting Room. *Abstract:* Labeling videos is costly, time-consuming, and tedious. These costs can escalate in applications such as medical diagnosis, etc. where we need domain expertise for annotation. Few-shot learning in Videos aims to solve this problem by building annotation-efficient learning mechanisms. This thesis presents MetaUVFS as the first Unsupervised Meta-learning algorithm for Video Few-Shot action recognition. MetaUVFS leverages over 550K unlabeled videos to train a two-stream 2D and 3D CNN architecture via contrastive learning to capture the appearance-specific spatial and action-specific Spatio-temporal video features respectively. MetaUVFS comprises a novel Action-Appearance Aligned Meta-adaptation~(A3M) module that learns to focus on the action-oriented video features in relation to the appearance features via explicit few-shot episodic meta-learning over unsupervised hard-mined episodes. Our action-appearance alignment and explicit few-shot learner conditions the unsupervised training to mimic the downstream few-shot task, enabling MetaUVFS to significantly outperform all state-of-the-art unsupervised methods on few-shot benchmarks. Moreover, unlike previous few-shot action recognition methods that are supervised, MetaUVFS needs neither base-class labels nor a supervised pre-trained backbone. Thus, we need to train MetaUVFS just once to perform competitively or sometimes even outperform state-of-the-art supervised methods on popular HMDB51, UCF101, and Kinetics100 few-shot datasets. *Advisor:* Dr. Fuxin Li *Committee:* Dr. Fuxin Li, Dr. Sinisa Todorovic, Dr. Stefan Lee, Dr. Yelda Turkan *Zoom Meeting:* Zoom Meeting details: Paravali, Jay Sanjay is inviting you to a scheduled Zoom meeting. Topic: Jay Patravali MS Robotics Thesis Defense. Time: Oct 26, 2021 12:00 PM Pacific Time (US and Canada) Join Zoom Meeting https://oregonstate.zoom.us/j/91663115146?pwd=bTBXQmFZa1VxM2N6emhxamhEenVrZ… Password: 943745 Phone Dial-In Information +1 971 247 1195 US (Portland) +1 253 215 8782 US (Tacoma) +1 301 715 8592 US (Washington DC) Meeting ID: 916 6311 5146 Join by Polycom/Cisco/Other Room System 91663115146(a)zoomcrc.com *EECS Department Response on serving Snacks or Coffee:* "The current OSU policy does not allow for serving of such items for events unless they are individually catered. Occupants would need to supply their own coffee and cookies." Thanks, Jay Patravali

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AI Seminar postponed
by Prasad Tadepalli 25 Oct '21

25 Oct '21

All, Due to some scheduling conflicts, this week's AI seminar on 10/27 by Lihong Li is postponed to November 10. There will not be a seminar this week, but there will be a seminar by Caelan Garrett on November 3 which will be posted at https://eecs.oregonstate.edu/ai-events Prasad

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AI seminar: Oct 27 2021
by Mangannavar, Rajesh Devaraddi 22 Oct '21

22 Oct '21

Dear all, Our next AI seminar on *"* *Estimating Long-term Rewards by Off-policy Reinforcement Learning" *by Lihong Li is scheduled to be on October 27th, 1-2 PM PST. It will be followed by a 30 minute Q&A session by the graduate students. Note that this is a zoom event. Zoom Link: https://oregonstate.zoom.us/j/93591935144?pwd=YjZaSjBYS0NmNUtjQzBEdzhPeDZ5U… *Estimating Long-term Rewards by Off-policy Reinforcement Learning * Lihong Li Senior Principal Scientist Amazon *Abstract*: One of the core problems in reinforcement learning (RL) is estimating the long-term reward of a given policy. In many real-world applications such as healthcare, robotics and dialogue systems, running a new policy on users or robots can be costly or risky. This gives rise to the need for off-policy, or counterfactual, estimation: estimate the long-term reward of a given policy using data previously collected by another policy (e.g., the one currently deployed). This talk will describe some recent advances in this problem, for which many standard estimators suffer an exponentially large variance (known as "the curse of horizon"). Our approach is based on a dual linear program formulation of the long-term reward, and can be extended to estimate confidence intervals. *Bio*: Lihong Li is a Senior Principal Scientist at Amazon. He obtained a PhD degree in Computer Science from Rutgers University. After that, he held research positions in Yahoo!, Microsoft and Google, before joining Amazon. His main research interests are in reinforcement learning, including contextual bandits, and other related problems in AI. His work is often inspired by applications in recommendation, advertising, Web search and conversational systems. Homepage: http://lihongli.github.io *Please watch this space for future AI Seminars :* * https://eecs.oregonstate.edu/ai-events <https://eecs.oregonstate.edu/ai-events>* *The video recordings of the first 3 AI seminars are also linked from the corresponding seminar pages available at the above link.* Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> The AI Seminar has a strict "no electronics" and "no recordings" policy. -> For graduate students in the AI program, attendance is strongly encouraged.

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AIGSA - Guide to Graduate School
by Slyman, Eric 21 Oct '21

21 Oct '21

Hi Folks, The *A*rtificial* I*ntelligence *G*raduate *S*tudent *A*ssociation (AIGSA) will be hosting a "*Guide to Graduate School*" seminar* tomorrow (October 21st)* from *4:00 PM to 5:00 PM* in* KEC 1001*. This seminar will share insights on common challenges in a student's journey from the experience of several EECS Professors. Be sure to arrive early and grab some cookies and coffee before the event! It will include three short talks: 1. "Navigating Your Graduate Program" - Prof. Stefan Lee <http://web.engr.oregonstate.edu/~leestef/> 2. "Conducting Academic Research" - Prof. Thomas Dietterich <https://eecs.oregonstate.edu/people/dietterich-thomas> 3. "Advisor-Advisee relationship" - Prof. Alan Fern <https://eecs.oregonstate.edu/people/fern-alan> RSVP here <https://forms.gle/f74HFPJnMSUopdLV8> to let us know if you're coming! To join our mailing list for future events, please follow the instructions at https://www.aigsa.club/. The event will be live-streamed over Zoom for those who wish to attend virtually. Details may be found below. *Zoom Meeting:* https://oregonstate.zoom.us/j/93786891383?pwd=Z3FKaUtTOHpiU0V5aUV4RDRGSUV2Q… *Meeting ID:* 937 8689 1383 *Password:* 24f51U7S Best, Eric Slyman (they/he) Communications Officer, AIGSA

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AI seminar: Oct 20 2021
by Mangannavar, Rajesh Devaraddi 19 Oct '21

19 Oct '21

Dear all, Our next AI seminar on *"From Escalation to Production" *by Javad Azimi is scheduled to be on October 20th (Tomorrow), 1-2 PM PST. It will be followed by a 30 minute Q&A session by the graduate students. Note that this is a zoom event. Zoom Link: https://oregonstate.zoom.us/j/93591935144?pwd=YjZaSjBYS0NmNUtjQzBEdzhPeDZ5U… *From Escalation to Production* Javad Azimi Machine Learning Engineer Twitter *Abstract:*Few years ago, I was sitting at the other side of the table at OSU where I was eagerly and happily listening to the people from outside of OSU sharing their research with amazing results. Top-notch researchers who moved (and are moving) the edge on science; I am not one of them now. I always wanted to go through the same path but I finally convinced myself to change the gear and move toward the product side in tech companies rather than research. World is much different here as the challenges are moved from ML sides to the product sides. How a problem is being defined and brought into our attention? How do we measure and evaluate the potential impact of allocating resources (human and budget)? How to prototype and make conclusions based on it? How to handle the scale and pave the path into having a product? And how to maintain and keep it up and running? In this talk, I am going to share my experiences in all of these steps with you with an interesting example: Misinformation Detection. My goal is to give you a taste of work waiting for you if you decide to go through the same path as I did. *Speaker Bio:*Javad Azimi is currently a tech lead ML engineer at Twitter working at the Misinformation team where the goal is to reduce the prevalence of misleading information at Twitter! Prior to joining Twitter in March 2020, he was serving the same role at Facebook with special focus on Facebook/Instagram posts containing photos and videos. Before Facebook, he worked at Microsoft where he developed NLP models for ads retrieval team for 4 years. He graduated from the EECS department of Oregon State University under the supervision of Xiaoli Fern and Alan Fern in 2012. His PhD thesis title was “Constrained Bayesian Optimization” where they published a few papers at MLJ, NIPS, ICML, KDD, AAAI, IJCAI, ECML etc. Please sign up here to meet (virtually) with the AI Seminar speaker Javad Azimi tomorrow. Multiple people can sign up in each slot. https://docs.google.com/document/d/1YnTLwMAuNLkFEUJFkIXKAiAxW5dyGUTzw4zwEuK… *Please watch this space for future AI Seminars: * * https://eecs.oregonstate.edu/ai-events <https://eecs.oregonstate.edu/ai-events>* Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> The AI Seminar has a strict "no electronics" and "no recordings" policy. -> For graduate students in the AI program, attendance is strongly encouraged.

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Re: [Ai] [CoRIS-List] Reminder: Robotics roundtable discussion, Friday October 15
by Knight, Heather M 15 Oct '21

15 Oct '21

There will also be a robotics program T-shirt distribution for those who can attend in person! Best, Prof. Knight On Fri, Oct 15, 2021 at 8:48 AM Hatton, Ross <Ross.Hatton(a)oregonstate.edu> wrote: > Hi All, > > As a reminder, we have a roundtable discussion for the Robotics Program > today. This event will be organized for both in-person and online > participation, with the online portion at > > > https://oregonstate.zoom.us/j/93958424124?pwd=bVlhZlRCbTFOaHIwZUx1R1h6d205Z… > . > > -Ross > > > Title: Roundtable Discussion and Q/A about the OSU Robotics Program > Friday October 15th, 10am-11am > LINC 302 > Speakers: Robotics program director and other Robotics faculty > > The robotics program director and other faculty will be available to > introduce ourselves and to host a roundtable discussion on the current > state of the OSU Robotics program. We encourage new students and existing > students to attend and ask any questions they may have about the program. > Some faculty will also be available afterwards to talk in smaller groups > with students. > _______________________________________________ > Ai mailing list > Ai(a)engr.orst.edu > https://it.engineering.oregonstate.edu/mailman/listinfo/ai > -- Assistant Professor of Computer Science CHARISMA Robotics Lab Collaborative Robots and Intelligent Systems (CoRIS) Institute Oregon State University

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[CoRIS-List] Reminder: Robotics roundtable discussion, Friday October 15
by Ross L. Hatton 15 Oct '21

15 Oct '21

Hi All, As a reminder, we have a roundtable discussion for the Robotics Program today. This event will be organized for both in-person and online participation, with the online portion at https://oregonstate.zoom.us/j/93958424124?pwd=bVlhZlRCbTFOaHIwZUx1R1h6d205Z…. -Ross Title: Roundtable Discussion and Q/A about the OSU Robotics Program Friday October 15th, 10am-11am LINC 302 Speakers: Robotics program director and other Robotics faculty The robotics program director and other faculty will be available to introduce ourselves and to host a roundtable discussion on the current state of the OSU Robotics program. We encourage new students and existing students to attend and ask any questions they may have about the program. Some faculty will also be available afterwards to talk in smaller groups with students.

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AI seminar: Oct 20 2021
by Mangannavar, Rajesh Devaraddi 15 Oct '21

15 Oct '21

Dear all, Our next AI seminar on *"From Escalation to Production" *by Javad Azimi is scheduled to be on October 20th, 1-2 PM PST. It will be followed by a 30 minute Q&A session by the graduate students. Note that this is a zoom event. Zoom Link: https://oregonstate.zoom.us/j/93591935144?pwd=YjZaSjBYS0NmNUtjQzBEdzhPeDZ5U… *From Escalation to Production* Javad Azimi Machine Learning Engineer Twitter *Abstract:*Few years ago, I was sitting at the other side of the table at OSU where I was eagerly and happily listening to the people from outside of OSU sharing their research with amazing results. Top notch researchers who moved (and are moving) the edge on science; I am not one of them now. I always wanted to go through the same path but I finally convinced myself to change the gear and move toward the product side in tech companies rather than research. World is much different here as the challenges are moved from ML sides to the product sides. How a problem is being defined and brought into our attention? How do we measure and evaluate the potential impact of allocating resources (human and budget)? How to prototype and make conclusions based on it? How to handle the scale and pave the path into having a product? And how to maintain and keep it up and running? In this talk, I am going to share my experiences in all of these steps with you with an interesting example: Misinformation Detection. My goal is to give you a taste of work waiting for you if you decide to go through the same path as I did. *Speaker Bio:*Javad Azimi is currently a tech lead ML engineer at Twitter working at the Misinformation team where the goal is to reduce the prevalence of misleading information at Twitter! Prior to joining Twitter in March 2020, he was serving the same role at Facebook with special focus on Facebook/Instagram posts containing photos and videos. Before Facebook, he worked at Microsoft where he developed NLP models for ads retrieval team for 4 years. He graduated from the EECS department of Oregon State University under the supervision of Xiaoli Fern and Alan Fern in 2012. His PhD thesis title was “Constrained Bayesian Optimization” where they published a few papers at MLJ, NIPS, ICML, KDD, AAAI, IJCAI, ECML etc. *Please watch this space for future AI Seminars: * * https://eecs.oregonstate.edu/ai-events <https://eecs.oregonstate.edu/ai-events>* Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> The AI Seminar has a strict "no electronics" and "no recordings" policy. -> For graduate students in the AI program, attendance is strongly encouraged.

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AI seminar: Oct 13 2021
by Mangannavar, Rajesh Devaraddi 12 Oct '21

12 Oct '21

Dear all, Our next AI seminar on *"From Heatmaps to Structural Explanations" *by professor Fuxin Li is scheduled to be on October 13th(Tomorrow), 1-2 PM PST. It will be followed by a 30 minute Q&A session by the graduate students. Note that this is an in-person/zoom hybrid event and will be held at *CORD 1109*. Zoom Link: https://oregonstate.zoom.us/j/93591935144?pwd=YjZaSjBYS0NmNUtjQzBEdzhPeDZ5U… *From Heatmaps to Structural Explanations* Fuxin Li Associate Professor Computer Science Oregon State University *Abstract*This talk will focus on our endeavors in the past few years in terms of explaining image classifiers. Realizing that an important missing piece for explaining neural networks is a reliable heatmap visualization tool, we developed I-GOS and iGOS++ utilizing integrated gradients to avoid local optima in heatmap generations and improve performance in high-resolution heatmaps. Especially, iGOS++ was able to discover that deep classifiers trained on COVID-19 X-ray images wrongly focus on the characters printed on the image and could produce erroneous solutions. This shows the utility of explanation in "debugging" the classifiers. During the development of those visualizations, we realize that for a significant number of images, the classifier has multiple different paths to reach a confident prediction, this leads to our recent development of structural attention graphs, an approach that utilizes beam search to locate multiple coarse heatmaps for a single image, and compactly visualizes a set of heatmaps by capturing how different combinations of image regions impact the confidence of a classifier. A user study shows significantly better capability of users to answer counterfactual questions when presented with SAG versus conventional heatmaps. *Speaker Bio:*Fuxin Li is currently an associate professor in the School of Electrical Engineering and Computer Science at Oregon State University. Before that, he has held research positions in University of Bonn and Georgia Institute of Technology. He had obtained a PhD degree in the Institute of Automation, Chinese Academy of Sciences in 2009. He has won an NSF CAREER award, an Amazon Research Award, an Oregon State University College of Engineering Research Award, (co-)won the PASCAL VOC semantic segmentation challenges from 2009-2012, and led a team to the 4th place finish in the DAVIS Video Segmentation challenge 2017. He has published more than 60 papers in computer vision, machine learning and natural language processing. His main research interests are deep learning, video object segmentation, multi-target tracking, point cloud deep networks, uncertainty estimation in deep learning and human understanding of deep learning. This talk will be followed by a talk on the topic "*From Escalation to Production*" by Javad Azimi on 20th October (Wednesday) 2021, 1-2 PM. *Please watch this space for future AI Seminars: * * https://eecs.oregonstate.edu/ai-events <https://eecs.oregonstate.edu/ai-events>* Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> The AI Seminar has a strict "no electronics" and "no recordings" policy. -> For graduate students in the AI program, attendance is strongly encouraged.

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