Summaries - Office of Research & Innovation
Research Summaries
Back Deep Learning for Detecting Anomalous Activity
Fiscal Year | 2019 |
Division | Graduate School of Engineering & Applied Science |
Department | Mechanical & Aerospace Engineering |
Investigator(s) |
Ross, Isaac M.
Karpenko, Mark |
Sponsor | Department of Defense Space (DoD) |
Summary | We propose to design a parallelizable deep learning algorithm for detecting anomalous activity that meets or exceeds human levels of accuracy. The technical objective will be met by a new machine learning approach that separates the optimization process from the backpropagation step in deep neural nets (DNNs). In sharp contrast to the current state of practice that involves sweeping back the gradients to determine the differential change in the loss function, our proposed algorithm minimizes a function that is related to the loss function in machine learning. |
Keywords | Deep Learning deep neural networks supervised learning algorithms |
Publications | Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal |
Data | Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal |