5 citations · 5 across the 3 of their papers we have counts for
4 papers
Uninorm-like parametric activation functions for human-understandable neural models
Orsolya Csiszár, Luca Sára Pusztaházi, Lehel Dénes-Fazakas +3
We present a deep learning model for finding human-understandable connections between input features. Our approach uses a parameterized, differentiable activation function, based o…
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…
Deep Learning in Robotics: A Review of Recent Research
Harry A. Pierson, Michael S. Gashler
Advances in deep learning over the last decade have led to a flurry of research in the application of deep artificial neural networks to robotic systems, with at least thirty paper…