Iterative Non-linear Dimensionality Reduction with Manifold Sculpting. - Michael Gashler; Dan Ventura; Tony R. Martinez (NIPS, 2007)

Decision Tree Ensemble: Small Heterogeneous Is Better Than Large Homogeneous. - Michael Gashler; Christophe G. Giraud-Carrier; Tony R. Martinez (ICMLA, 2008)

Temporal nonlinear dimensionality reduction. - Michael Gashler; Tony R. Martinez (IJCNN, 2011)

Tangent space guided intelligent neighbor finding. - Michael Gashler; Tony R. Martinez (IJCNN, 2011)

Waffles: A Machine Learning Toolkit. - Michael Gashler (J. Mach. Learn. Res., 2011)

Manifold Learning by Graduated Optimization. - Michael Gashler; Dan Ventura; Tony R. Martinez (IEEE Trans. Syst. Man Cybern. Part B, 2011)

Robust manifold learning with CycleCut. - Michael Gashler; Tony R. Martinez (Connect. Sci., 2012)

Training Deep Fourier Neural Networks to Fit Time-Series Data. - Michael S. Gashler; Stephen C. Ashmore (ICIC (3), 2014)

A Continuum among Logarithmic, Linear, and Exponential Functions, and Its Potential to Improve Generalization in Neural Networks. - Luke B. Godfrey; Michael S. Gashler (KDIR, 2015)

A method for finding similarity between multi-layer perceptrons by Forward Bipartite Alignment. - Stephen C. Ashmore; Michael S. Gashler (IJCNN, 2015)

A minimal architecture for general cognition. - Michael S. Gashler; Zachariah Kindle; Michael R. Smith 002 (IJCNN, 2015)

A hybrid latent variable neural network model for item recommendation. - Michael R. Smith 002; Michael S. Gashler; Tony R. Martinez (IJCNN, 2015)

Practical Techniques for Using Neural Networks to Estimate State from Images. - Stephen C. Ashmore; Michael S. Gashler (ICMLA, 2016)

Missing Value Imputation with Unsupervised Backpropagation. - Michael S. Gashler; Michael R. Smith 002; Richard G. Morris; Tony R. Martinez (Comput. Intell., 2016)

Modeling time series data with deep Fourier neural networks. - Michael S. Gashler; Stephen C. Ashmore (Neurocomputing, 2016)

An Investigation of How Neural Networks Learn from the Experiences of Peers Through Periodic Weight Averaging. - Joshua Smith; Michael Gashler (ICMLA, 2017)

A parameterized activation function for learning fuzzy logic operations in deep neural networks. - Luke B. Godfrey; Michael S. Gashler (SMC, 2017)

Neural decomposition of time-series data. - Luke B. Godfrey; Michael S. Gashler (SMC, 2017)

Deep learning in robotics: a review of recent research. - Harry A. Pierson; Michael S. Gashler (Adv. Robotics, 2017)

Learning Resolution-independent Image Representations. - Jon C. Hammer; Michael S. Gashler (ICCI*CC, 2018)

Leveraging Product as an Activation Function in Deep Networks. - Luke B. Godfrey; Michael Gashler (SMC, 2018)

Neural Decomposition of Time-Series Data for Effective Generalization. - Luke B. Godfrey; Michael S. Gashler (IEEE Trans. Neural Networks Learn. Syst., 2018)

Parametric activation functions modelling fuzzy connectives for better explainability of neural models. - Luca Sára Pusztaházi; Gábor Csiszár; Michael S. Gashler; Orsolya Csiszár (SISY, 2022)

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)