24 citations · 131 across the 25 of their papers we have counts for
7 papers · 1 filter
Exploiting Heterogeneity in Operational Neural Networks by Synaptic Plasticity
Serkan Kiranyaz, Junaid Malik, Habib Ben Abdallah +3
The recently proposed network model, Operational Neural Networks (ONNs), can generalize the conventional Convolutional Neural Networks (CNNs) that are homogenous only with a linear…
FastONN -- Python based open-source GPU implementation for Operational Neural Networks
Junaid Malik, Serkan Kiranyaz, Moncef Gabbouj
Operational Neural Networks (ONNs) have recently been proposed as a special class of artificial neural networks for grid structured data. They enable heterogenous non-linear operat…
Neural Architecture Search by Estimation of Network Structure Distributions
Anton Muravev, Jenni Raitoharju, Moncef Gabbouj
The influence of deep learning is continuously expanding across different domains, and its new applications are ubiquitous. The question of neural network design thus increases in…
Finding Better Topologies for Deep Convolutional Neural Networks by Evolution
Honglei Zhang, Serkan Kiranyaz, Moncef Gabbouj
Due to the nonlinearity of artificial neural networks, designing topologies for deep convolutional neural networks (CNN) is a challenging task and often only heuristic approach, su…
Progressive Operational Perceptron with Memory
Dat Thanh Tran, Serkan Kiranyaz, Moncef Gabbouj +1
Generalized Operational Perceptron (GOP) was proposed to generalize the linear neuron model in the traditional Multilayer Perceptron (MLP) and this model can mimic the synaptic con…
Heterogeneous Multilayer Generalized Operational Perceptron
Dat Thanh Tran, Serkan Kiranyaz, Moncef Gabbouj +1
The traditional Multilayer Perceptron (MLP) using McCulloch-Pitts neuron model is inherently limited to a set of neuronal activities, i.e., linear weighted sum followed by nonlinea…