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个人简介
Dr. Baraniuk's research interests in signal processing and machine learning lie primarily in new theory and algorithms involving low-dimensional models. His research on theory of deep learning, compressive sensing, multiscale natural image modeling using wavelet-domain hidden Markov models, and time-frequency analysis has been funded by NSF, DARPA, ONR, AFOSR, AFRL, ARO, IARPA, DOE, NGA, EPA, NATO, the Texas Instruments Leadership University Program, and several companies. In particular, he has served as Project Director for the ARO MURI on "Opportunistic Sensing" from 2013-2018, the ONR MURI on "Foundations of Deep Learning" from 2020-2025, the DARPA/DOE "INCITE" project, and several DARPA projects, including "Analog to Information," "Analog to Information Receiver," and "Network Modeling and Simulation." He was a member of the DARPA Information Science and Technology (ISAT) Study Group from 2008-2011. More information on his signal processing and machine learning research.
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论文共 829 篇作者统计合作学者相似作者
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CoRR (2024)
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CoRR (2024)
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arxiv(2023)
LAK (2023): 828-835
Micah Goldblum,Anima Anandkumar,Richard Baraniuk,Tom Goldstein, Kyunghyun Cho, Zachary C Lipton, Melanie Mitchell, Preetum Nakkiran, Max Welling,Andrew Gordon Wilson
CoRR (2023)
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