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Found 8 papers, 1 papers with code
Uninorm-like parametric activation functions for human-understandable neural models
We present a deep learning model for finding human-understandable connections between input features.
Leveraging Product as an Activation Function in Deep Networks
We present windowed product unit neural networks (WPUNNs), a simple method of leveraging product as a nonlinearity in a neural network.
A parameterized activation function for learning fuzzy logic operations in deep neural networks
We present a deep learning architecture for learning fuzzy logic expressions.
Neural Decomposition of Time-Series Data for Effective Generalization
We present a neural network technique for the analysis and extrapolation of time-series data called Neural Decomposition (ND).
A continuum among logarithmic, linear, and exponential functions, and its potential to improve generalization in neural networks
We present the soft exponential activation function for artificial neural networks that continuously interpolates between logarithmic, linear, and exponential functions.
A Minimal Architecture for General Cognition
From this perspective, MANIC offers an alternate approach to a long-standing objective of artificial intelligence.
Training Deep Fourier Neural Networks To Fit Time-Series Data
We present a method for training a deep neural network containing sinusoidal activation functions to fit to time-series data.
Missing Value Imputation With Unsupervised Backpropagation
We evaluate UBP with the task of imputing missing values in datasets, and show that UBP is able to predict missing values with significantly lower sum-squared error than other collaborative filtering and imputation techniques.
