Michael S. Gashler

According to our database, Michael S. Gashler authored at least 24 papers between 2007 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Uninorm-like parametric activation functions for human-understandable neural models.

Orsolya Csiszár, Luca Sára Pusztaházi, Lehel Dénes-Fazakas, Michael S. Gashler, Vladik Kreinovich, Gábor Csiszár

Knowl. Based Syst., 2023

2022

Parametric activation functions modelling fuzzy connectives for better explainability of neural models.

Luca Sára Pusztaházi, Gábor Csiszár, Michael S. Gashler, Orsolya Csiszár

Proceedings of the 20th Jubilee International Symposium on Intelligent Systems and Informatics, 2022

2018

Neural Decomposition of Time-Series Data for Effective Generalization.

Luke B. Godfrey, Michael S. Gashler

IEEE Trans. Neural Networks Learn. Syst., 2018

Leveraging Product as an Activation Function in Deep Networks.

Luke B. Godfrey, Michael Gashler

Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 2018

Learning Resolution-independent Image Representations.

Jon C. Hammer, Michael S. Gashler

Proceedings of the 17th IEEE International Conference on Cognitive Informatics & Cognitive Computing, 2018

2017

Deep learning in robotics: a review of recent research.

Harry A. Pierson, Michael S. Gashler

Adv. Robotics, 2017

Neural decomposition of time-series data.

Luke B. Godfrey, Michael S. Gashler

Proceedings of the 2017 IEEE International Conference on Systems, Man, and Cybernetics, 2017

A parameterized activation function for learning fuzzy logic operations in deep neural networks.

Luke B. Godfrey, Michael S. Gashler

Proceedings of the 2017 IEEE International Conference on Systems, Man, and Cybernetics, 2017

An Investigation of How Neural Networks Learn from the Experiences of Peers Through Periodic Weight Averaging.

Joshua Smith, Michael Gashler

Proceedings of the 16th IEEE International Conference on Machine Learning and Applications, 2017

2016

Modeling time series data with deep Fourier neural networks.

Michael S. Gashler, Stephen C. Ashmore

Neurocomputing, 2016

Missing Value Imputation with Unsupervised Backpropagation.

Michael S. Gashler, Michael R. Smith, Richard G. Morris, Tony R. Martinez

Comput. Intell., 2016

Practical Techniques for Using Neural Networks to Estimate State from Images.

Stephen C. Ashmore, Michael S. Gashler

Proceedings of the 15th IEEE International Conference on Machine Learning and Applications, 2016

2015

A hybrid latent variable neural network model for item recommendation.

Michael R. Smith, Michael S. Gashler, Tony R. Martinez

Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

A minimal architecture for general cognition.

Michael S. Gashler, Zachariah Kindle, Michael R. Smith

Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

A method for finding similarity between multi-layer perceptrons by Forward Bipartite Alignment.

Stephen C. Ashmore, Michael S. Gashler

Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

A Continuum among Logarithmic, Linear, and Exponential Functions, and Its Potential to Improve Generalization in Neural Networks.

Luke B. Godfrey, Michael S. Gashler

Proceedings of the KDIR 2015, 2015

2014

Training Deep Fourier Neural Networks to Fit Time-Series Data.

Michael S. Gashler, Stephen C. Ashmore

Proceedings of the Intelligent Computing in Bioinformatics - 10th International Conference, 2014

2012

Robust manifold learning with CycleCut.

Michael Gashler, Tony R. Martinez

Connect. Sci., 2012

2011

Manifold Learning by Graduated Optimization.

Michael Gashler, Dan Ventura, Tony R. Martinez

IEEE Trans. Syst. Man Cybern. Part B, 2011

<i>Waffles</i>: A Machine Learning Toolkit.

Michael Gashler

J. Mach. Learn. Res., 2011

Tangent space guided intelligent neighbor finding.

Michael Gashler, Tony R. Martinez

Proceedings of the 2011 International Joint Conference on Neural Networks, 2011

Temporal nonlinear dimensionality reduction.

Michael Gashler, Tony R. Martinez

Proceedings of the 2011 International Joint Conference on Neural Networks, 2011

2008

Decision Tree Ensemble: Small Heterogeneous Is Better Than Large Homogeneous.

Michael Gashler, Christophe G. Giraud-Carrier, Tony R. Martinez

Proceedings of the Seventh International Conference on Machine Learning and Applications, 2008

2007

Iterative Non-linear Dimensionality Reduction with Manifold Sculpting.

Michael Gashler, Dan Ventura, Tony R. Martinez

Proceedings of the Advances in Neural Information Processing Systems 20, 2007