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ORCIDMichael S. Gashler
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2020 – today
- 2023
[j8]Orsolya Csiszár
, Luca Sára Pusztaházi, Lehel Dénes-Fazakas, Michael S. Gashler, Vladik Kreinovich, Gábor Csiszár:
Uninorm-like parametric activation functions for human-understandable neural models. Knowl. Based Syst. 260: 110095 (2023)- 2022
- [c16]Luca Sára Pusztaházi, Gábor Csiszár, Michael S. Gashler, Orsolya Csiszár:
Parametric activation functions modelling fuzzy connectives for better explainability of neural models. SISY 2022: 77-82 - [i10]Orsolya Csiszár, Luca Sára Pusztaházi, Lehel Dénes-Fazakas, Michael S. Gashler, Vladik Kreinovich, Gábor Csiszár:
Uninorm-like parametric activation functions for human-understandable neural models. CoRR abs/2205.06547 (2022)
2010 – 2019
- 2018
- [j7]Luke B. Godfrey, Michael S. Gashler:
Neural Decomposition of Time-Series Data for Effective Generalization. IEEE Trans. Neural Networks Learn. Syst. 29(7): 2973-2985 (2018) - [c15]Jon C. Hammer, Michael S. Gashler:
Learning Resolution-independent Image Representations. ICCI*CC 2018: 408-416 - [c14]Luke B. Godfrey, Michael Gashler:
Leveraging Product as an Activation Function in Deep Networks. SMC 2018: 1617-1622 - [i9]Luke B. Godfrey, Michael S. Gashler:
Leveraging Product as an Activation Function in Deep Networks. CoRR abs/1810.08578 (2018) - 2017
- [j6]Harry A. Pierson, Michael S. Gashler:
Deep learning in robotics: a review of recent research. Adv. Robotics 31(16): 821-835 (2017) - [c13]Joshua Smith, Michael Gashler:
An Investigation of How Neural Networks Learn from the Experiences of Peers Through Periodic Weight Averaging. ICMLA 2017: 731-736 - [c12]Luke B. Godfrey, Michael S. Gashler:
A parameterized activation function for learning fuzzy logic operations in deep neural networks. SMC 2017: 740-745 - [c11]Luke B. Godfrey, Michael S. Gashler:
Neural decomposition of time-series data. SMC 2017: 2796-2801 - [i8]Luke B. Godfrey, Michael S. Gashler:
Neural Decomposition of Time-Series Data for Effective Generalization. CoRR abs/1705.09137 (2017) - [i7]Harry A. Pierson, Michael S. Gashler:
Deep Learning in Robotics: A Review of Recent Research. CoRR abs/1707.07217 (2017) - [i6]Luke B. Godfrey, Michael S. Gashler:
A parameterized activation function for learning fuzzy logic operations in deep neural networks. CoRR abs/1708.08557 (2017) - 2016
- [j5]Michael S. Gashler, Michael R. Smith, Richard G. Morris, Tony R. Martinez:
Missing Value Imputation with Unsupervised Backpropagation. Comput. Intell. 32(2): 196-215 (2016) - [j4]Michael S. Gashler, Stephen C. Ashmore:
Modeling time series data with deep Fourier neural networks. Neurocomputing 188: 3-11 (2016) - [c10]Stephen C. Ashmore, Michael S. Gashler:
Practical Techniques for Using Neural Networks to Estimate State from Images. ICMLA 2016: 916-919 - [i5]Luke B. Godfrey, Michael S. Gashler:
A continuum among logarithmic, linear, and exponential functions, and its potential to improve generalization in neural networks. CoRR abs/1602.01321 (2016) - 2015
- [c9]Luke B. Godfrey, Michael S. Gashler:
A Continuum among Logarithmic, Linear, and Exponential Functions, and Its Potential to Improve Generalization in Neural Networks. KDIR 2015: 481-486 - [c8]Stephen C. Ashmore, Michael S. Gashler:
A method for finding similarity between multi-layer perceptrons by Forward Bipartite Alignment. IJCNN 2015: 1-7 - [c7]Michael S. Gashler, Zachariah Kindle, Michael R. Smith:
A minimal architecture for general cognition. IJCNN 2015: 1-8 - [c6]Michael R. Smith, Michael S. Gashler, Tony R. Martinez:
A hybrid latent variable neural network model for item recommendation. IJCNN 2015: 1-7 - [i4]Michael S. Gashler, Zachariah Kindle, Michael R. Smith:
A Minimal Architecture for General Cognition. CoRR abs/1508.00019 (2015) - 2014
- [c5]Michael S. Gashler, Stephen C. Ashmore:
Training Deep Fourier Neural Networks to Fit Time-Series Data. ICIC (3) 2014: 48-55 - [i3]Michael S. Gashler, Stephen C. Ashmore:
Training Deep Fourier Neural Networks To Fit Time-Series Data. CoRR abs/1405.2262 (2014) - [i2]Michael R. Smith, Tony R. Martinez, Michael Gashler:
A Hybrid Latent Variable Neural Network Model for Item Recommendation. CoRR abs/1406.2235 (2014) - 2013
- [i1]Michael Gashler, Michael R. Smith, Richard G. Morris, Tony R. Martinez:
Missing Value Imputation With Unsupervised Backpropagation. CoRR abs/1312.5394 (2013) - 2012
- [j3]Michael Gashler, Tony R. Martinez:
Robust manifold learning with CycleCut. Connect. Sci. 24(1): 57-69 (2012) - 2011
- [j2]Michael Gashler:
Waffles: A Machine Learning Toolkit. J. Mach. Learn. Res. 12: 2383-2387 (2011) - [j1]Michael Gashler, Dan Ventura, Tony R. Martinez:
Manifold Learning by Graduated Optimization. IEEE Trans. Syst. Man Cybern. Part B 41(6): 1458-1470 (2011) - [c4]Michael Gashler, Tony R. Martinez:
Temporal nonlinear dimensionality reduction. IJCNN 2011: 1959-1966 - [c3]Michael Gashler, Tony R. Martinez:
Tangent space guided intelligent neighbor finding. IJCNN 2011: 2617-2624
2000 – 2009
- 2008
- [c2]Michael Gashler, Christophe G. Giraud-Carrier, Tony R. Martinez:
Decision Tree Ensemble: Small Heterogeneous Is Better Than Large Homogeneous. ICMLA 2008: 900-905 - 2007
- [c1]Michael Gashler, Dan Ventura, Tony R. Martinez:
Iterative Non-linear Dimensionality Reduction with Manifold Sculpting. NIPS 2007: 513-520
Coauthor Index
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