| Cyclical learning rates for training neural networks LN Smith 2017 IEEE winter conference on applications of computer vision (WACV), 464-472, 2017 | 3153 | 2017 |
| Super-convergence: Very fast training of neural networks using large learning rates LN Smith, N Topin Artificial intelligence and machine learning for multi-domain operations …, 2019 | 1444 | 2019 |
| A disciplined approach to neural network hyper-parameters: Part 1--learning rate, batch size, momentum, and weight decay LN Smith arXiv preprint arXiv:1803.09820, 2018 | 1228 | 2018 |
| Improving dictionary learning: Multiple dictionary updates and coefficient reuse LN Smith, M Elad IEEE Signal Processing Letters 20 (1), 79-82, 2012 | 159 | 2012 |
| A disciplined approach to neural network hyper-parameters: Part 1—Learning rate, batch size, momentum, and weight decay. arXiv 2018 LN Smith arXiv preprint arXiv:1803.09820, 1803 | 138 | 1803 |
| Rotational compound state resonances for an argon and methane scattering system LN Smith, DJ Malik, D Secrest The Journal of Chemical Physics 71 (11), 4502-4514, 1979 | 105 | 1979 |
| Deep convolutional neural network design patterns LN Smith, N Topin arXiv preprint arXiv:1611.00847, 2016 | 81 | 2016 |
| Close‐coupling and coupled state calculations of argon scattering from normal methane LN Smith, D Secrest The Journal of Chemical Physics 74 (7), 3882-3897, 1981 | 59 | 1981 |
| An approach to explainable deep learning using fuzzy inference D Bonanno, K Nock, L Smith, P Elmore, F Petry Next-Generation Analyst V 10207, 132-136, 2017 | 45 | 2017 |
| Gradual dropin of layers to train very deep neural networks LN Smith, EM Hand, T Doster Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 33 | 2016 |
| Restoration of turbulence degraded underwater images AV Kanaev, W Hou, S Woods, LN Smith Optical Engineering 51 (5), 057007-057007, 2012 | 32 | 2012 |
| A Disciplined Approach to Neural Network Hyper-Parameters: Part 1–Learning Rate LN Smith Batch size, Momentum, and Weight decay 8, 1803, 2018 | 30 | 2018 |
| Disambiguation protocols based on risk simulation DE Fishkind, CE Priebe, KE Giles, LN Smith, V Aksakalli IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and …, 2007 | 30 | 2007 |
| Exploring loss function topology with cyclical learning rates LN Smith, N Topin arXiv preprint arXiv:1702.04283, 2017 | 27 | 2017 |
| Selecting subgoals using deep learning in minecraft: A preliminary report D Bonanno, M Roberts, L Smith, DW Aha IJCAI workshop on deep learning for artificial intelligence 32, 2016 | 16 | 2016 |
| Method of estimating blur kernel from edge profiles in a blurry image LN Smith US Patent 8,594,447, 2013 | 11 | 2013 |
| Best practices for applying deep learning to novel applications LN Smith arXiv preprint arXiv:1704.01568, 2017 | 10 | 2017 |
| Estimating an image’s blur kernel from edge intensity profiles L Smith Naval research laboratory, 2012 | 9 | 2012 |
| Denoising infrared maritime imagery using tailored dictionaries via modified K-SVD algorithm LN Smith, CC Olson, KP Judd, JM Nichols Applied Optics 51 (17), 3941-3949, 2012 | 9 | 2012 |
| Building one-shot semi-supervised (BOSS) learning up to fully supervised performance LN Smith, A Conovaloff Frontiers in Artificial Intelligence 5, 880729, 2022 | 8 | 2022 |
David W. AhaDirector, Navy Center for Applied Research in AI, Naval Research LaboratoryПотвърден имейл адрес: nrl.navy.mil
