28 citations · 55 across the 4 of their papers we have counts for
4 papers
Training from Zero: Radio Frequency Machine Learning Data Quantity Forecasting
William H. Clark, Alan J. Michaels
The data used during training in any given application space is directly tied to the performance of the system once deployed. While there are many other factors that go into produc…
When is Enough Enough? "Just Enough" Decision Making with Recurrent Neural Networks for Radio Frequency Machine Learning
Megan Moore, William H. Clark, R. Michael Buehrer +1
Prior work has demonstrated that recurrent neural network architectures show promising improvements over other machine learning architectures when processing temporally correlated…
The RFML Ecosystem: A Look at the Unique Challenges of Applying Deep Learning to Radio Frequency Applications
Lauren J. Wong, William H. Clark, Bryse Flowers +3
While deep machine learning technologies are now pervasive in state-of-the-art image recognition and natural language processing applications, only in recent years have these techn…
Training Data Augmentation for Deep Learning Radio Frequency Systems
William H. Clark, Steven Hauser, William C. Headley +1
Applications of machine learning are subject to three major components that contribute to the final performance metrics. Within the category of neural networks, and deep learning s…