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Full-Text Articles in Engineering

Natural Language Processing For Novel Writing, Leqing Qu, Okan Ersoy Sep 2022

Natural Language Processing For Novel Writing, Leqing Qu, Okan Ersoy

Department of Electrical and Computer Engineering Technical Reports

No abstract provided.


Predicting The Flight Of A Golf Ball: Comparing A Physics-Based Aerodynamic Model To A Neural Network, Spencer Ferguson, William Mcnally, John Mcphee Jun 2022

Predicting The Flight Of A Golf Ball: Comparing A Physics-Based Aerodynamic Model To A Neural Network, Spencer Ferguson, William Mcnally, John Mcphee

International Sports Engineering Association – Engineering of Sport

No abstract provided.


Discrepancies Among Pre-Trained Deep Neural Networks: A New Threat To Model Zoo Reliability, Diego Montes, Pongpatapee Peerapatanapokin, Jeff Schultz, Chengjun Guo, Wenxin Jiang, James C. Davis Jan 2022

Discrepancies Among Pre-Trained Deep Neural Networks: A New Threat To Model Zoo Reliability, Diego Montes, Pongpatapee Peerapatanapokin, Jeff Schultz, Chengjun Guo, Wenxin Jiang, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

Training deep neural networks (DNNs) takes significant time and resources. A practice for expedited deployment is to use pre-trained deep neural networks (PTNNs), often from model zoos.collections of PTNNs; yet, the reliability of model zoos remains unexamined. In the absence of an industry standard for the implementation and performance of PTNNs, engineers cannot confidently incorporate them into production systems. As a first step, discovering potential discrepancies between PTNNs across model zoos would reveal a threat to model zoo reliability. Prior works indicated existing variances in deep learning systems in terms of accuracy. However, broader measures of reliability for PTNNs from …