58 citations · 67 across the 8 of their papers we have counts for
6 papers · 1 filter
Leveraging Product as an Activation Function in Deep Networks
Luke B. Godfrey, Michael S. Gashler
Product unit neural networks (PUNNs) are powerful representational models with a strong theoretical basis, but have proven to be difficult to train with gradient-based optimizers.…
A parameterized activation function for learning fuzzy logic operations in deep neural networks
Luke B. Godfrey, Michael S. Gashler
We present a deep learning architecture for learning fuzzy logic expressions. Our model uses an innovative, parameterized, differentiable activation function that can learn a numbe…
Neural Decomposition of Time-Series Data for Effective Generalization
Luke B. Godfrey, Michael S. Gashler
We present a neural network technique for the analysis and extrapolation of time-series data called Neural Decomposition (ND). Units with a sinusoidal activation function are used…
A continuum among logarithmic, linear, and exponential functions, and its potential to improve generalization in neural networks
Luke B. Godfrey, Michael S. Gashler
We present the soft exponential activation function for artificial neural networks that continuously interpolates between logarithmic, linear, and exponential functions. This activ…
Training Deep Fourier Neural Networks To Fit Time-Series Data
Michael S. Gashler, Stephen C. Ashmore
We present a method for training a deep neural network containing sinusoidal activation functions to fit to time-series data. Weights are initialized using a fast Fourier transform…
Missing Value Imputation With Unsupervised Backpropagation
Michael S. Gashler, Michael R. Smith, Richard Morris +1
Many data mining and data analysis techniques operate on dense matrices or complete tables of data. Real-world data sets, however, often contain unknown values. Even many classific…