| Bayesopt adversarial attack B Ru, A Cobb, A Blaas, Y Gal International Conference on Learning Representations, 2019 | 48 | 2019 |
| An ensemble of bayesian neural networks for exoplanetary atmospheric retrieval AD Cobb, MD Himes, F Soboczenski, S Zorzan, MD O’Beirne, AG Baydin, ... The astronomical journal 158 (1), 33, 2019 | 41 | 2019 |
| Loss-calibrated approximate inference in Bayesian neural networks AD Cobb, SJ Roberts, Y Gal arXiv preprint arXiv:1805.03901, 2018 | 41 | 2018 |
| Torsional guided wave attenuation in piping from coating, temperature, and large-area corrosion AC Cobb, H Kwun, L Caseres, G Janega NDT & E International 47, 163-170, 2012 | 36 | 2012 |
| A comparison of feature-based classifiers for ultrasonic structural health monitoring JE Michaels, AC Cobb, TE Michaels Health Monitoring and Smart Nondestructive Evaluation of Structural and …, 2004 | 35 | 2004 |
| Model-assisted probability of detection for ultrasonic structural health monitoring C Adam, J Fisher, JE Michaels Proceedings of the 4th European-American Workshop on Reliability of NDE …, 2009 | 29 | 2009 |
| An automated time–frequency approach for ultrasonic monitoring of fastener hole cracks AC Cobb, JE Michaels, TE Michaels NDT & E International 40 (7), 525-536, 2007 | 27 | 2007 |
| Review of magnetostrictive transducers (MsT) utilizing reversed Wiedemann effect S Vinogradov, A Cobb, G Light AIP Conference Proceedings 1806 (1), 020008, 2017 | 20 | 2017 |
| Self‐Calibrating Ultrasonic Methods for In‐Situ Monitoring of Fatigue Crack Progression JE Michaels, TE Michaels, B Mi, AC Cobb, DM Stobbe AIP Conference Proceedings 760 (1), 1765-1772, 2005 | 19 | 2005 |
| Ultrasonic structural health monitoring: a probability of detection case study AC Cobb, JE Michaels, TE Michaels AIP Conference Proceedings 1096 (1), 1800-1807, 2009 | 17 | 2009 |
| New magnetostrictive transducer designs for emerging application areas of NDE S Vinogradov, A Cobb, J Fisher Materials 11 (5), 755, 2018 | 15 | 2018 |
| Introducing an explicit symplectic integration scheme for Riemannian manifold Hamiltonian Monte Carlo AD Cobb, AG Baydin, A Markham, SJ Roberts arXiv preprint arXiv:1910.06243, 2019 | 14 | 2019 |
| Nonlinear ultrasonic measurements with EMATs for detecting pre-cracking fatigue damage A Cobb, M Capps, C Duffer, J Feiger, K Robinson, B Hollingshaus AIP Conference Proceedings 1430 (1), 299-306, 2012 | 13 | 2012 |
| HumBug Zooniverse: a crowd-sourced acoustic mosquito dataset I Kiskin, AD Cobb, L Wang, S Roberts ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 11 | 2020 |
| Bayesian deep neural networks for low-cost neurophysiological markers of Alzheimer's disease severity W Fruehwirt, AD Cobb, M Mairhofer, L Weydemann, H Garn, R Schmidt, ... arXiv preprint arXiv:1812.04994, 2018 | 11 | 2018 |
| Flaw depth sizing using guided waves AC Cobb, JL Fisher AIP Conference Proceedings 1706 (1), 030013, 2016 | 9 | 2016 |
| Simultaneous ultrasonic monitoring of crack growth and dynamic loads during a full scale fatigue test of an aircraft wing TE Michaels, JE Michaels, AC Cobb AIP Conference Proceedings 1096 (1), 1458-1465, 2009 | 8 | 2009 |
| The practicalities of scaling Bayesian neural networks to real-world applications AD Cobb University of Oxford, 2020 | 7 | 2020 |
| Development of a novel omnidirectional magnetostrictive transducer for plate applications S Vinogradov, A Cobb, J Bartlett, Y Udagawa AIP Conference Proceedings 1949 (1), 090002, 2018 | 7 | 2018 |
| Detecting sensitization in aluminum alloys using acoustic resonance and EMAT ultrasound A Cobb, E Macha, J Bartlett, Y Xia AIP Conference Proceedings 1806 (1), 050001, 2017 | 7 | 2017 |
Jennifer E. MichaelsProfessor Emerita, Electrical and Computer Engineering, Georgia Institute of TechnologyVerified email at gatech.edu
