Voting with Random Neural Networks: a Democratic Ensemble Classifier

This paper aims at revisiting Convolutional Graph Neural Networks (ConvGNNs) by designing new graph convolutions in spectral domain with a cus- tom frequency profile while applying

Rubino, “Echo State Queueing Networks: a combination of Reservoir Computing and Random Neural Networks”, Prob.. in

In the general case of feed-forward neural networks (including convolutional deep neural network architectures), under random noise at- tacks, we propose to study the probability

We have been able to repli- cate these findings on random recurrent neural networks (RRNN) with a classical hebbian learning rule [7].. Following Amari [1], and using a

1.3 Why expert systems, fuzzy systems, neural networks, and hybrid systems for knowledge engineering and problem

● Deep Neural Networks: applicable to classification problems with non-linear decision boundaries. ● Visualize prediction from fixed

Unfortunately, these works have not fully established Newton methods as a practical technique for deep learning because other types of networks such as Convolutional Neural

We propose a taxonomy classifier based on sequence- to-sequence neural networks, which are widely used in machine translation and automatic document summarization, by treating