13 citations · 20 across the 3 of their papers we have counts for
4 papers
Continuous Learning in a Single-Incremental-Task Scenario with Spike Features
Ruthvik Vaila, John Chiasson, Vishal Saxena
Deep Neural Networks (DNNs) have two key deficiencies, their dependence on high precision computing and their inability to perform sequential learning, that is, when a DNN is train…
A Deep Unsupervised Feature Learning Spiking Neural Network with Binarized Classification Layers for EMNIST Classification using SpykeFlow
Ruthvik Vaila, John Chiasson, Vishal Saxena
End user AI is trained on large server farms with data collected from the users. With ever increasing demand for IOT devices, there is a need for deep learning approaches that can…
Regression with Deep Learning for Sensor Performance Optimization
Ruthvik Vaila, Denver Lloyd, Kevin Tetz
Neural networks with at least two hidden layers are called deep networks. Recent developments in AI and computer programming in general has led to development of tools such as Tens…
Deep Convolutional Spiking Neural Networks for Image Classification
Ruthvik Vaila, John Chiasson, Vishal Saxena
Spiking neural networks are biologically plausible counterparts of the artificial neural networks, artificial neural networks are usually trained with stochastic gradient descent a…