Michael S. Gashler

23 papers B 5C 1Misc 1Journal 13Unranked 3
YearRankTypeTitle / Venue / Authors
2023 J jnl
Uninorm-like parametric activation functions for human-understandable neural models.
Knowl. Based Syst.
Orsolya Csiszár, Luca Sára Pusztaházi, Lehel Dénes-Fazakas, Michael S. Gashler, Vladik Kreinovich, Gábor Csiszár
2022 Misc conf
Parametric activation functions modelling fuzzy connectives for better explainability of neural models.
SISY
Luca Sára Pusztaházi, Gábor Csiszár, Michael S. Gashler, Orsolya Csiszár
2022 J jnl
Uninorm-like parametric activation functions for human-understandable neural models.
CoRR
Orsolya Csiszár, Luca Sára Pusztaházi, Lehel Dénes-Fazakas, Michael S. Gashler, Vladik Kreinovich, Gábor Csiszár
2018 — conf
Learning Resolution-independent Image Representations.
ICCI*CC
Jon C. Hammer, Michael S. Gashler
2018 J jnl
Leveraging Product as an Activation Function in Deep Networks.
CoRR
Luke B. Godfrey, Michael S. Gashler
2018 J jnl
Neural Decomposition of Time-Series Data for Effective Generalization.
IEEE Trans. Neural Networks Learn. Syst.
Luke B. Godfrey, Michael S. Gashler
2017 B conf
A parameterized activation function for learning fuzzy logic operations in deep neural networks.
SMC
Luke B. Godfrey, Michael S. Gashler
2017 J jnl
A parameterized activation function for learning fuzzy logic operations in deep neural networks.
CoRR
Luke B. Godfrey, Michael S. Gashler
2017 J jnl
Deep Learning in Robotics: A Review of Recent Research.
CoRR
Harry A. Pierson, Michael S. Gashler
2017 J jnl
Deep learning in robotics: a review of recent research.
Adv. Robotics
Harry A. Pierson, Michael S. Gashler
2017 J jnl
Neural Decomposition of Time-Series Data for Effective Generalization.
CoRR
Luke B. Godfrey, Michael S. Gashler
2017 B conf
Neural decomposition of time-series data.
SMC
Luke B. Godfrey, Michael S. Gashler
2016 J jnl
A continuum among logarithmic, linear, and exponential functions, and its potential to improve generalization in neural networks.
CoRR
Luke B. Godfrey, Michael S. Gashler
2016 J jnl
Missing Value Imputation with Unsupervised Backpropagation.
Comput. Intell.
Michael S. Gashler, Michael R. Smith, Richard G. Morris, Tony R. Martinez
2016 J jnl
Modeling time series data with deep Fourier neural networks.
Neurocomputing
Michael S. Gashler, Stephen C. Ashmore
2016 C conf
Practical Techniques for Using Neural Networks to Estimate State from Images.
ICMLA
Stephen C. Ashmore, Michael S. Gashler
2015 — conf
A Continuum among Logarithmic, Linear, and Exponential Functions, and Its Potential to Improve Generalization in Neural Networks.
KDIR
Luke B. Godfrey, Michael S. Gashler
2015 J jnl
A Minimal Architecture for General Cognition.
CoRR
Michael S. Gashler, Zachariah Kindle, Michael R. Smith
2015 B conf
A hybrid latent variable neural network model for item recommendation.
IJCNN
Michael R. Smith, Michael S. Gashler, Tony R. Martinez
2015 B conf
A method for finding similarity between multi-layer perceptrons by Forward Bipartite Alignment.
IJCNN
Stephen C. Ashmore, Michael S. Gashler
2015 B conf
A minimal architecture for general cognition.
IJCNN
Michael S. Gashler, Zachariah Kindle, Michael R. Smith
2014 J jnl
Training Deep Fourier Neural Networks To Fit Time-Series Data.
CoRR
Michael S. Gashler, Stephen C. Ashmore
2014 — conf
Training Deep Fourier Neural Networks to Fit Time-Series Data.
ICIC (3)
Michael S. Gashler, Stephen C. Ashmore