IE-Net: Information-Enhanced Binary Neural Networks for Accurate Classification

XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks

We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks. In Binary-WeightNetworks, the filters are approximated with binary values resulting in 32× memory saving. In XNOR-Networks, both the filters and the input to convolutional layers are binary. XNOR-Networks approximate convolutions using primarily binary operations. This re...

Understanding the Loss Surface of Neural Networks for Binary Classification

It is widely conjectured that the reason that training algorithms for neural networks are successful because all local minima lead to similar performance; for example, see [1, 2, 3]. Performance is typically measured in terms of two metrics: training performance and generalization performance. Here we focus on the training performance of neural networks for binary classification, and provide co...

Constructing feed-forward neural networks for binary classification tasks

Ensemble of Duo Output Neural Networks For Binary Classification

This paper presents an ensemble of duo output neural networks (DONN) using bagging technique to solve binary classification problems. DONN is a neural network that is trained to predict a pair of complementary outputs which are the truth and falsity values. Each component in an ensemble contains two DONNs in which the first network is trained to predict the truth and falsity outputs whereas the...

Binary classification using ensemble neural networks and interval neutrosophic sets

This paper presents an ensemble neural network and interval neutrosophic sets approach to the problem of binary classification. A bagging technique is applied to an ensemble of pairs of neural networks created to predict degree of truth membership, indeterminacy membership, and false membership values in the interval neutrosophic sets. In our approach, the error and vagueness are quantified in ...