| Deep learning in robotics: a review of recent research HA Pierson, MS Gashler Advanced Robotics 31 (16), 821-835, 2017 | 388 | 2017 |
| Decision tree ensemble: Small heterogeneous is better than large homogeneous M Gashler, C Giraud-Carrier, T Martinez 2008 Seventh international conference on machine learning and applications …, 2008 | 213 | 2008 |
| Neural decomposition of time-series data for effective generalization LB Godfrey, MS Gashler IEEE transactions on neural networks and learning systems 29 (7), 2973-2985, 2017 | 76 | 2017 |
| Iterative non-linear dimensionality reduction with manifold sculpting M Gashler, D Ventura, T Martinez Advances in Neural Information Processing Systems, 513-520, 2008 | 61 | 2008 |
| Waffles: A Machine Learning Toolkit M Gashler The Journal of Machine Learning Research 12, 2383-2387, 2011 | 54 | 2011 |
| Modeling time series data with deep Fourier neural networks MS Gashler, SC Ashmore Neurocomputing 188, 3-11, 2016 | 52 | 2016 |
| A Continuum among Logarithmic, Linear, and Exponential Functions, and Its Potential to Improve Generalization in Neural Networks LB Godfrey, MS Gashler Proceedings of the International Conference on Knowledge Discovery and …, 2015 | 49 | 2015 |
| Training Deep Fourier Neural Networks To Fit Time-Series Data MS Gashler, SC Ashmore Lecture Notes in Computer Science 8590 (10th International Conference, ICIC …, 2014 | 48 | 2014 |
| Security requirement determination S Lange, GD Fee, A Goldfeder, I Medvedev, M Gashler US Patent 7,743,423, 2010 | 42 | 2010 |
| Hosted code runtime protection CW Brumme, S Lange, GD Fee, M Gashler, M Prakriya US Patent 7,647,629, 2010 | 24 | 2010 |
| A method for finding similarity between multi-layer perceptrons by forward bipartite alignment S Ashmore, M Gashler 2015 International Joint Conference on Neural Networks (IJCNN), 1-7, 2015 | 21 | 2015 |
| Robust manifold learning with CycleCut M Gashler, T Martinez Connection Science 24 (1), 57-69, 2012 | 20 | 2012 |
| Manifold learning by graduated optimization M Gashler, D Ventura, T Martinez Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 1–13, 2011 | 20 | 2011 |
| An investigation of how neural networks learn from the experiences of peers through periodic weight averaging J Smith, M Gashler 2017 16th IEEE International Conference on Machine Learning and Applications …, 2017 | 19 | 2017 |
| Temporal nonlinear dimensionality reduction M Gashler, T Martinez The 2011 international joint conference on neural networks, 1959-1966, 2011 | 19 | 2011 |
| A parameterized activation function for learning fuzzy logic operations in deep neural networks LB Godfrey, MS Gashler 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC …, 2017 | 17 | 2017 |
| Automatic clustering of sequential design behaviors MH Rahman, M Gashler, C Xie, Z Sha International Design Engineering Technical Conferences and Computers and …, 2018 | 16 | 2018 |
| Missing Value Imputation With Unsupervised Backpropagation MS Gashler, MR Smith, T Martinez, R Morris Computational Intelligence 32 (2), page 165, 2016 | 16 | 2016 |
| Uninorm-like parametric activation functions for human-understandable neural models O Csiszár, LS Pusztaházi, L Dénes-Fazakas, MS Gashler, V Kreinovich, ... Knowledge-Based Systems 260, 110095, 2023 | 10 | 2023 |
| Tangent space guided intelligent neighbor finding M Gashler, T Martinez The 2011 International Joint Conference on Neural Networks, 2617-2624, 2011 | 10 | 2011 |
Tony MartinezProfessor of Computer Science, Brigham Young Universityכתובת אימייל מאומתת בדומיין cs.byu.edu