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Found 4 papers, 1 papers with code
A Significantly Better Class of Activation Functions Than ReLU Like Activation Functions
This paper introduces a significantly better class of activation functions than the almost universally used ReLU like and Sigmoidal class of activation functions.
Efficient Vectorized Backpropagation Algorithms for Training Feedforward Networks Composed of Quadratic Neurons
Solutions to the XOR problem with a single quadratic neuron are considered.
Alternate Loss Functions for Classification and Robust Regression Can Improve the Accuracy of Artificial Neural Networks
Two new classification loss functions that significantly improve performance on a wide variety of benchmark tasks are proposed.
Growing Cosine Unit: A Novel Oscillatory Activation Function That Can Speedup Training and Reduce Parameters in Convolutional Neural Networks
This ability to learn complex high-dimensional functions hierarchically can be attributed to the use of nonlinear activation functions.
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