Category: Machine learning
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talk: Iterative Preconditioning for Accelerating Machine Learning Problems, 12-1 4/27
ArtIAMAS Seminar SeriesCo-organized by UMBC, UMCP, and Army Research Lab Iterative Preconditioning forAccelerating Machine Learning Problems Nikhil Chopra Mechanical Engineering, UMCP 12-1 ET Wed. 27 April 2022, WebEx We study a new approach to accelerating machine learning problems in this talk. The system comprises multiple agents, each with a set of local data points and…
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Prof. Matuszek receives prestigious NSF CAREER award for robotics research
CSEE professor Cynthia Matuszek received a presigeous NSF CAREER award to support her research on improving the ability of robots to interact with people in everyday environments. The five-year award provides nearly $550,000 to support research by Dr. Matuszek and her students in the Interactive Robotics and Language lab.
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talk: Top-K Ranking Deep Contextual Bandits for Information Selection Systems, 12pm ET 12/8
ArtIAMAS Seminar Series, co-organized by UMBC, UMCP & Army Research Lab Top-K Ranking Deep Contextual Bandits for Information Selection Systems Dr. Jade Freeman, Army Research Lab 12-1pm ET Wed. 8 Dec. 2021, Online via Webex In today’s technology environment, information is abundant, dynamic, and heterogeneous in nature. Automated filtering and prioritization of information is based on the…
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talk: Human-in-the-Loop Entity Mining from Noisy Web Data, 1-2 4/6
Human-in-the-Loop Entity Mining from Noisy Web Data Professor Eduard Dragut, Temple University 1-2 pm, Tuesday, 6 April 2021online via WebEx Recognizing entities that follow or closely resemble a regular expression (regex) pattern is an important task in information extraction. Due to a vast diversity of web documents and ways in which they are generated, even seemingly…
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talk: Forward & Inverse Causal Inference in a Tensor Framework, 1-2 pm ET, 3/29
Forward and Inverse Causal Inference in a Tensor Framework M. Alex O. VasilescuInstitute of Pure and Applied Mathematics, UCLA 1-2:00 pm Monday, March 29, 2021via WebEx Developing causal explanations for correct results or for failures from mathematical equations and data is important in developing a trustworthy artificial intelligence, and retaining public trust. Causal explanations are…
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talk: Machine Learning: New Methodology for Physical & Social Sciences, 1pm ET 3/24
24 hour LIDAR backscatter profiles and PBLH points generated from image machine learning system The Infusion of Machine Learning as a New Methodology for the Physical and Social Sciences Dr. Jennifer SleemanCSEE, UMBC 1:00-2:00 pm ET, Wednesday, March 24Online via WebEx Machine learning has made improvements in many areas of computing. Recently attention has been given…
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talk: (Don’t) Mind the Gap: Bridging the Worlds of People and IoT Devices, 1-2 ET 3/23
Dr. Roberto Yus talks about his research helping IoT systems bridge the gap between the world of IoT devices and the world where people act. 1-2 pm ET, Tue. March 23
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talk: Towards Contextual Security of AI-enabled platforms, 1-2 pm ET 3/22
Dr. Nidhi Rastogi of RPI talks about her work on using automated, trustworthy, and contextual AI systems to improve the security of Internet-connected systems and devices.
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talk: Theoryful Machine Learning in the Chemical Sciences, 1-2 Fri 2/5
Prof. Tyler Josephson of UMBC’s Chemical, Biochemical & Environmental Engineering Dept. will talk on Theoryful Machine Learning in the Chemical Sciences, 1-2pm ET Friday, Feb. 5.
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Visiting Prof. Ed Raff’s forthcoming book: Inside Deep Learning
Ed Raff’s book Inside Deep Learning is being published by Manning. He’s a Chief Scientist at Booz Allen Hamilton and both an alumnus of and visiting assistant professor in the UMBC CSEE department.