Michael S. Gashler

Publication Activity (10 Years)

Years Active: 2014-2023
Publications (10 Years): 16

Top Topics

Activation Function

Multilayer Perceptron

Image Representation

Top Venues

Publications

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 (2023)
Luca Sára Pusztaházi, Gábor Csiszár, Michael S. Gashler, Orsolya Csiszár
Parametric activation functions modelling fuzzy connectives for better explainability of neural models. SISY (2022)
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 (2022)
Luke B. Godfrey, Michael S. Gashler
Neural Decomposition of Time-Series Data for Effective Generalization. IEEE Trans. Neural Networks Learn. Syst. 29 (7) (2018)
Jon C. Hammer, Michael S. Gashler
Learning Resolution-independent Image Representations. ICCI*CC (2018)
Luke B. Godfrey, Michael S. Gashler
Leveraging Product as an Activation Function in Deep Networks. CoRR (2018)
Luke B. Godfrey, Michael S. Gashler
A parameterized activation function for learning fuzzy logic operations in deep neural networks. CoRR (2017)
Luke B. Godfrey, Michael S. Gashler
A parameterized activation function for learning fuzzy logic operations in deep neural networks. SMC (2017)
Harry A. Pierson, Michael S. Gashler
Deep Learning in Robotics: A Review of Recent Research. CoRR (2017)
Luke B. Godfrey, Michael S. Gashler
Neural Decomposition of Time-Series Data for Effective Generalization. CoRR (2017)
Harry A. Pierson, Michael S. Gashler
Deep learning in robotics: a review of recent research. Adv. Robotics 31 (16) (2017)
Luke B. Godfrey, Michael S. Gashler
Neural decomposition of time-series data. SMC (2017)
Stephen C. Ashmore, Michael S. Gashler
Practical Techniques for Using Neural Networks to Estimate State from Images. ICMLA (2016)
Luke B. Godfrey, Michael S. Gashler
A continuum among logarithmic, linear, and exponential functions, and its potential to improve generalization in neural networks. CoRR (2016)
Michael S. Gashler, Michael R. Smith, Richard G. Morris, Tony R. Martinez
Missing Value Imputation with Unsupervised Backpropagation. Comput. Intell. 32 (2) (2016)
Michael S. Gashler, Stephen C. Ashmore
Modeling time series data with deep Fourier neural networks. Neurocomputing 188 (2016)
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)
Michael S. Gashler, Zachariah Kindle, Michael R. Smith
A minimal architecture for general cognition. IJCNN (2015)
Michael R. Smith, Michael S. Gashler, Tony R. Martinez
A hybrid latent variable neural network model for item recommendation. IJCNN (2015)
Michael S. Gashler, Zachariah Kindle, Michael R. Smith
A Minimal Architecture for General Cognition. CoRR (2015)
Stephen C. Ashmore, Michael S. Gashler
A method for finding similarity between multi-layer perceptrons by Forward Bipartite Alignment. IJCNN (2015)
Michael S. Gashler, Stephen C. Ashmore
Training Deep Fourier Neural Networks to Fit Time-Series Data. ICIC (3) (2014)
Michael S. Gashler, Stephen C. Ashmore
Training Deep Fourier Neural Networks To Fit Time-Series Data. CoRR (2014)