Talk: Visual Concept Learning Beyond Appearances, 3:30pm 2/8
Modernizing a couple of classic ideas
PPR Distinguished Speaker
Visual Concept Learning Beyond Appearances: Modernizing a Couple of Classic Ideas
3:30-4:45 pm ET, Thur. Feb. 8, 2024
ITE 325b & via WebEx
The goal of
Computer Vision, as coined by
Marr, is to develop algorithms to answer "What are", "Where at", "When from" visual appearance. The speaker, among others, recognizes the importance of studying underlying entities and relations beyond visual appearance, following an Active Perception paradigm. This talk will present the speaker's efforts over the last decade, ranging from 1) reasoning beyond appearance for vision and language tasks (VQA, captioning, T2I, etc.), and addressing their evaluation misalignment, through 2) reasoning about implicit properties, to 3) their roles in a Robotic visual concept learning framework. The talk will also feature the Active Perception Group (APG)'s projects addressing emerging challenges of the nation in automated mobility and intelligent transportation domains, at the ASU School of Computing and Augmented Intelligence (SCAI).
Yezhou (YZ) Yang is an Associate Professor and a Fulton Entrepreneurial Professor in the School of Computing and Augmented Intelligence (SCAI) at Arizona State University. He founded and directs the ASU Active Perception Group, and currently serves as the topic lead (situation awareness) at the Institute of Automated Mobility, Arizona Commerce Authority. He is also a thrust lead (AVAI) at Advanced Communications Technologies (ACT, a Science and Technology Center under the New Economy Initiative, Arizona). His work includes exploring visual primitives and representation learning in visual (and language) understanding, grounding them by natural language and high-level reasoning over the primitives for intelligent systems, secure/robust AI, and V&L model evaluation alignment. Yang is a recipient of the Qualcomm Innovation Fellowship 2011, the NSF CAREER award 2018, and the Amazon AWS Machine Learning Research Award 2019. He received his Ph.D. from the University of Maryland at College Park, and B.E. from Zhejiang University, China. He is a co- founder of ARGOS Vision Inc, an ASU spin-off company.
The Advances in Perception, Prediction, and Reasoning (PPR) talks are organized and hosted by UMBC Professor
Tejas Gokhale.
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PPR Distinguished Speaker Visual Concept Learning Beyond Appearances: Modernizing a Couple of Classic Ideas Dr. Yezhou Yang Arizona State University 3:30-4:45 pm ET, Thur. Feb. 8, 2024...
https://www.tejasgokhale.com/seminar.html
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UMBC AI
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Advances in Perception, Prediction, and Reasoning Lab
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Wed, 31 Jan 2024 14:22:06 -0500
Tue, 27 Feb 2024 17:40:25 -0500
New Center for Applied AI at UMBC Training Centers
Its curated courses & micro-courses invite you to unravel AI
UMBC Training Centers established a new
Center for Applied AI as a dedicated hub for advancing skills and fostering innovation in the ever-changing field of Artificial Intelligence.
It will offer multiple learning opportunities through courses, webinars, and micro-sessions from a team of experienced instructors with extensive industry knowledge.
The center will also provide a variety of free programming, including virtual information sessions, Q&As, workshops, and in-person community meet-ups to accommodate different skill levels and professional backgrounds.
Check out its 27-minute video Introducing Generative AI, ChatGPT, & more, and sign up for its upcoming free AI Lunchbox talks held 12-1 pm every few weeks.
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UMBC Training Centers established a new Center for Applied AI as a dedicated hub for advancing skills and fostering innovation in the ever-changing field of Artificial Intelligence. It will...
https://c4a.ai/
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Tue, 30 Jan 2024 10:41:27 -0500
Tue, 27 Feb 2024 17:47:46 -0500
Graduate student sought for NLP/Speech project
Opportunity for an hourly research position
Dr. Shimei Pan from the Information Systems department is looking for a graduate student to work on an NLP and spoken language processing project. This position will start immediately and last for about three months. The compensation is $25 per hour for a maximum of 20 hours per week.
Minimum Qualifications:
- Strong programming skills in Python
- Experience in developing deep learning models using PyTorch and/or TensorFlow
- Minimum GPA of 3.7
- Passionate about research and can work independently
Preferred Qualifications:
- Knowledge of NLP, speech processing, video processing or sensor technologies is a plus
- Familiar with audio editing tools
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Dr. Shimei Pan from the Information Systems department is looking for a graduate student to work on an NLP and spoken language processing project. This position will start immediately and last for...
https://nlp-lab.umbc.edu/home-2/
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Sat, 27 Jan 2024 09:59:47 -0500
UMBC CARTA Center for data analytics research
NSF continues funding the IUCRC for five years
UMBC is partnering with Rutgers University, the University of Miami, and Arizona State University to deploy CARTA Phase II. The focus is on data analytics research to develop new accelerated and real-time approaches to address advanced technology integration in industries such as health care, finance, and security.
Current projects at UMBC include legal text analytics, optimizing semantic knowledge graph systems on GPUs, using machine learning to improve understanding of weather and wildfire spreading, and developing better analytics to detect malware.
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UMBC's Center for Accelerated Real Time Analytics (CARTA) team will advance its work in investigating data analytics using next-generation computing hardware with the launch of the next phase of...
https://umbc.edu/stories/center-for-accelerated-real-time-analytics/
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Fri, 26 Jan 2024 11:20:54 -0500
Talk: Towards Democratizing AI, 12-1 Tue 1/30, ITE 325b
Scaling & Learning (Fair) Graph Representations
Towards Democratizing AI: Scaling & Learning (Fair) Graph Representations in an Implementation Agnostic Fashion
12-1 pm ET, Tue. 30 January 2024, ITE 325b
Srinivasan Parthasarathy, Ohio State University
Recently there has been a surge of interest in designing graph embedding methods. Few, if any, can scale to a large-sized graph with millions of nodes due to both computational complexity and memory requirements. In this talk, I will present an approach to redress this limitation by introducing the MultI-Level Embedding (MILE) framework -- a generic methodology allowing con-temporary graph embedding methods to scale to large graphs. MILE repeatedly coarsens the graph into smaller ones using a hybrid matching technique to maintain the backbone structure of the graph. It then applies existing embedding methods on the coarsest graph and refines the embeddings to the original graph through a graph convolution neural network that it learns. Time permitting, I will then describe one of several natural extensions to MILE - in a distributed setting (DistMILE) to further improve the scalability of graph embedding or mechanisms - to learn fair graph representations (FairMILE).
The proposed MILE framework and variants (DistMILE, FairMILE), are agnostic to the underlying graph embedding techniques and can be applied to many existing graph embedding methods without modifying them and is agnostic to their implementation language. Experimental results on five large-scale datasets demonstrate that MILE significantly boosts the speed (order of magnitude) of graph embedding while generating embeddings of better quality, for the task of node classification. MILE can comfortably scale to a graph with 9 million nodes and 40 million edges, on which existing methods run out of memory or take too long to compute on a modern workstation. Our experiments demonstrate that DistMILE learns representations of similar quality with respect to other baselines while reducing the time of learning embeddings even further (up to 40 x speedup over MILE). FairMILE similarly learns fair representations of the data while reducing the time of learning embeddings.
Joint work with Jionqian Liang (Google Brain), S. Gurukar (OSU) and Yuntian He (OSU)
Bio: Srinivasan Parthasarathy is a Professor of Computer Science and Engineering and the director of the data mining research laboratory at Ohio State. His research interests span data analytics, databases and high-performance computing. He is among a handful of researchers nationwide to have won both the Department of Energy and National Science Foundation Career awards. He and his students have won multiple best paper awards or "best of" nominations from leading forums in the field including: SIAM Data Mining, ACM SIGKDD, VLDB, ISMB, WWW, ICDM, and ACM Bioinformatics. He chaired the SIAM data mining conference steering committee (elected) from 2012 till 2019, and has served on the board of several journals in parallel computing, machine learning and data mining. Since 2012 he also helped lead the creation of OSU's first-of-a-kind nationwide (US) undergraduate major in data analytics and serves as one of its founding directors.
Colleagues from across Maryland Higher Education institutions are invited to save the date for this virtual showcase, which will take place on Friday, April 26, 2024 from 10:00 a.m. - 1:00 p.m. The event will take place online via Zoom.
Registration will open in early March 2024 on this event page. To be notified when registration opens, please contact cai@usmd.edu. The sponsoring organizations will also notify their constituents when registration opens.
Faculty, staff, and faculty/staff/student teams from across Maryland Higher Education institutions who have engaged in the use of Generative AI as part of assignments and learning activities are invited to propose sessions for the Incorporating Generative AI into Learning Experiences Virtual Showcase, taking place via Zoom on Friday, April 26, 2024, from 10:00 am to 1:00 pm ET.
Deadline for Proposal Submission: Monday, February 12, 2024, 11:59 p.m. ET. See the call for proposals for more information.
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Colleagues from across Maryland Higher Education institutions are invited to save the date for this virtual showcase, which will take place on Friday, April 26, 2024 from 10:00 a.m. - 1:00...
https://www.usmd.edu/cai/incorporating-generative-ai-learning-experiences-virtual-showcase
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Wed, 17 Jan 2024 09:24:56 -0500
Search & Taxonomy Summer Internship, Idaho National Lab
Help change the world's energy future
Idaho National Laboratory is seeking an undergraduate or graduate student studying in a Library Science, Computer Science, or Information Science program. Candidates should have interest in taxonomies and ontologies and how they would support research. Candidates should also have an interest in metadata and digital records management. As a Search and Taxonomy Intern, you will play a crucial role in enhancing our information retrieval and organization processes. You will work closely with the Records and Document management team to normalize data for search, optimize search functionality and help create a robust taxonomy system, ensuring that our data is easily accessible and accurately categorized. This 2024 internship offers a unique opportunity to gain hands-on experience in search and taxonomy management.
More information on the internship is here.
AI4ALL Ignite offers a groundbreaking opportunity for undergraduate students interested in artificial intelligence. This no-cost, virtual AI career accelerator program is designed to prepare students to interview for technical AI internship positions. As part of the accelerator, participants work on an AI portfolio project directly with AI industry professionals, present the portfolio to peers and industry professionals at an all-day virtual symposium, and cultivate and grow a network within AI. Apply online by January 22.
Students who participate in the Spring 2024 accelerator will be ready to interview for AI technical internship positions by September 2024.
Key Highlights of AI4ALL Ignite:
AI Portfolio Project: Students will have the unique opportunity to work on an AI project with peers, receiving mentorship and guidance from industry experts.
Student Symposium: Participants can present their AI projects in a symposium, providing a platform to network with AI professionals.
Career Readiness Training: The program includes extensive training focusing on career readiness and preparation for technical AI internship interviews.
Mock Interviews and Networking: Students can enhance their skills through mock AI technical interviews and engage in networking opportunities with AI recruiters.
AI Certificate of Completion: Students are awarded an AI4ALL Certificate of Completion at end of the accelerator.
Eligibility and Application Deadlines:
AI4ALL welcomes all applicants but gives priority to students from historically underrepresented groups in AI, including Black, Hispanic and Latinx, Indigenous, women, gender-expansive, and non-binary individuls.
Academic prerequisites are: Probability and Statistics; Linear Algebra; Pursuing a major/minor related to Computer Science and similar academic disciplines; and Basic programming skills in Python (e.g. CS1) or another coding language
Apply online for the Spring 2024 Cohort by January 22, 2024.
The Spring Ignite program starts in the week of January 29, 2024.
For more information and to apply, visit the AI4ALL Ignite website. To learn more about the organization AI4ALL, please visit the AI4ALL website. Address further questions to ai4all@cs.umbc.edu.
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AI4ALL Ignite offers a groundbreaking opportunity for undergraduate students interested in artificial intelligence. This no-cost, virtual AI career accelerator program is designed to prepare...
https://ai-4-all.org/ai4all-ignite/
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Wed, 10 Jan 2024 09:25:44 -0500
Maryland governor signs executive order guiding AI use
Order creates an AI subcabinet to develop guardrail plans
Maryland Gov. Wes Moore (D) signed an executive order calling for the state to develop guardrails to protect residents from the risk of bias and discrimination as artificial intelligence becomes increasingly useful and common, though the order did not specify how the government intends to use AI in the future.
Moore's executive order establishes an AI subcabinet to ensure that state agencies follow a set of principles guiding the ethical and productive use of AI technologies. The subcabinet will also be tasked with identifying opportunities AI could offer to recruit or expand businesses, providing a needed jolt to the state's sluggish economy. Maryland officials on Monday additionally announced plans to modernize the state's digital infrastructure, expand access to online resources and combat cybersecurity threats.
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Maryland Gov. Wes Moore (D) signed an executive order calling for the state to develop guardrails to protect residents from the risk of bias and discrimination as artificial intelligence becomes...
https://www.washingtonpost.com/dc-md-va/2024/01/08/maryland-ai-government-wes-moore/
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Tue, 09 Jan 2024 11:03:50 -0500
Dr. Chenchen Liu interviewed about her AI hardware research
Developing better hardware models and architectures for AI
UMBC News interviewed CSEE professor Chenchen Liu about her research on developing better hardware models and architectures to support current and future AI systems, supported in part by a recent prestigious five-year NSF CAREER award. Read the interview here.
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UMBC News interviewed CSEE professor Chenchen Liu about her research on developing better hardware models and architectures to support current and future AI systems, supported in part by a recent...
https://umbc.edu/stories/building-next-gen-ai-chips/
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ai
computer-engineering
hardware
UMBC AI
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UMBC AI
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Tue, 26 Dec 2023 10:47:36 -0500
