Tensor Regression Networks. 2 Related work In the mathematics and engineering communities concerned with uncertainty quantification tensor. This repository contains the Tensorflow implementation of the IEEE TCAD paper A Computationally Efficient Tensor Regression Network based Modeling Attack on XOR Arbiter PUF and its Variants.
Tensor regression networks significantly reduce the number of effective parameters in deep neural networks while retaining accuracy and the ease of training. Abstract We propose a new Bayesian Markov switching regression model for multidimen- sional arrays tensors of binary time series. May 15 2020 Regression-based neural networks.
This notebook uses the classic Auto MPG Dataset and builds a.
Augmenting the VGG and ResNet architectures we demonstrate improved performance on the ImageNet dataset despite signi cantly reducing the number of parameters almost by 65. Tensor regression networks significantly reduce the number of effective parameters in deep neural networks while retaining accuracy and the ease of training. Convolutional neural networks typically consist of many convolutional layers followed by one or more fully connected layers. Several recent papers apply tensor decomposition to deep learning.
