Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Lauren J. Wong"'
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 6, Iss 3, Pp 1699-1719 (2024)
The use of transfer learning (TL) techniques has become common practice in fields such as computer vision (CV) and natural language processing (NLP). Leveraging prior knowledge gained from data with different distributions, TL offers higher performan
Externí odkaz:
https://doaj.org/article/17be79b1859f4f8593d083d7aa40496b
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 6, Iss 2, Pp 1210-1242 (2024)
Transfer learning (TL) techniques, which leverage prior knowledge gained from data with different distributions to achieve higher performance and reduced training time, are often used in computer vision (CV) and natural language processing (NLP), but
Externí odkaz:
https://doaj.org/article/43187fdd23d745d98281d08a5e5ec009
Publikováno v:
IEEE Access, Vol 12, Pp 80327-80344 (2024)
Performance of radio frequency machine learning (RFML) models for classification tasks such as specific emitter identification (SEI) and automatic modulation classification (AMC) have improved greatly since their introduction, especially when measure
Externí odkaz:
https://doaj.org/article/ff17706b2a594d7eac6f6adfa6eacb69
Publikováno v:
Sensors, Vol 24, Iss 11, p 3574 (2024)
Transfer learning (TL) techniques have proven useful in a wide variety of applications traditionally dominated by machine learning (ML), such as natural language processing, computer vision, and computer-aided design. Recent extrapolations of TL to t
Externí odkaz:
https://doaj.org/article/4757a137f2864f30aabd65ce4a872816
Publikováno v:
IEEE Access, Vol 11, Pp 110023-110038 (2023)
Generating high-quality, real-world, well-labeled datasets for radio frequency machine learning (RFML) applications often proves prohibitively cumbersome and expensive, leading to the low availability of high-fidelity, low-cost datasets. Specific emi
Externí odkaz:
https://doaj.org/article/1d86ee63f4f24e228c7ce3519b53bce8
Publikováno v:
IEEE Open Journal of the Communications Society, Vol 3, Pp 2076-2086 (2022)
Transfer learning (TL) has proven to be a transformative technology for computer vision (CV) and natural language processing (NLP) applications, offering improved generalization, state-of-the-art performance, and faster training time with less labell
Externí odkaz:
https://doaj.org/article/9e78ea48ded840c6bffe35b1e8b3a0d8
Autor:
Lauren J. Wong, William H. Clark, Bryse Flowers, R. Michael Buehrer, William C. Headley, Alan J. Michaels
Publikováno v:
IEEE Open Journal of the Communications Society, Vol 2, Pp 2243-2264 (2021)
While deep learning (DL) technologies are now pervasive in state-of-the-art Computer Vision (CV) and Natural Language Processing (NLP) applications, only in recent years have these technologies started to sufficiently mature in applications related t
Externí odkaz:
https://doaj.org/article/a6980177ff4749c88093ac595124fffc
Publikováno v:
IEEE Access, Vol 7, Pp 33544-33555 (2019)
Specific Emitter Identification is the association of a received signal to a unique emitter, and is made possible by the naturally occurring and unintentional characteristics an emitter imparts onto each transmission, known as its radio frequency fin
Externí odkaz:
https://doaj.org/article/25c3851e9cfd44fd9fc440fed2a79110
Autor:
Lauren J. Wong, Alan J. Michaels
Publikováno v:
Sensors, Vol 22, Iss 4, p 1416 (2022)
Transfer learning is a pervasive technology in computer vision and natural language processing fields, yielding exponential performance improvements by leveraging prior knowledge gained from data with different distributions. However, while recent wo
Externí odkaz:
https://doaj.org/article/fa0b2374786d4ba6b8d3efd018b71f36
Autor:
William H. Clark, Alan J. Michaels, Bryse Flowers, Lauren J. Wong, William C. Headley, R. Michael Buehrer
Publikováno v:
IEEE Open Journal of the Communications Society, Vol 2, Pp 2243-2264 (2021)
While deep learning (DL) technologies are now pervasive in state-of-the-art Computer Vision (CV) and Natural Language Processing (NLP) applications, only in recent years have these technologies started to sufficiently mature in applications related t