This paper presents a novel technique for texture modeling and synthesis using the random neural network (RNN). This technique is based on learning the weights of a recurrent network directly from the texture image. The same trained recurrent network is then used to generate a synthetic texture that imitates the original one. The proposed texture learning technique is very e cient and its compu...

While massive efforts have been investigated in adversarial testing of convolutional neural networks (CNN), for recurrent (RNN) is still limited and leaves threats vast sequential application domains. In this paper, we propose an framework RNN-Test RNN systems, focusing on sequence-to-sequence (seq2seq) tasks widespread deployments, not only classification First, design a novel search methodolo...

Long Short-Term Memory (LSTM) is a specific recurrent neural network (RNN) architecture that was designed to model temporal sequences and their long-range dependencies more accurately than conventional RNNs. In this paper, we explore LSTM RNN architectures for large scale acoustic modeling in speech recognition. We recently showed that LSTM RNNs are more effective than DNNs and conventional RNN...

This paper describes a novel text-to-speech (TTS) technique based on deep convolutional neural networks (CNN), without any recurrent units. Recurrent neural network (RNN) has been a standard technique to model sequential data recently, and this technique has been used in some cutting-edge neural TTS techniques. However, training RNN component often requires a very powerful computer, or very lon...

In this paper, we use variational recurrent model to investigate the time series forecasting problem. Combining recurrent neural network (RNN) and variational inference (VI), this model has both deterministic hidden states and stochastic latent variables while previous RNN methods only consider deterministic states. Based on comprehensive experiments, we show that the proposed methods significa...

The Random Neural Network (RNN) is a recurrent neural network model inspired by the spiking behaviour of biological neuronal networks. Contrary to most Artificial Neural Networks (ANN) models, neurons in RNN interact by probabilistically exchanging excitatory and inhibitory spiking signals. The model is described by analytical equations, has a low complexity supervised learning algorithm and is...

In this paper, we propose a generic technique to model temporal dependencies and sequences using a combination of a recurrent neural network and a Deep Belief Network. Our technique, RNN-DBN, is an amalgamation of the memory state of the RNN that allows it to provide temporal information and a multi-layer DBN that helps in high level representation of the data. This makes RNN-DBNs ideal for seq...

In this paper, we consider the scene parsing problem and propose a novel MultiPath Feedback recurrent neural network (MPF-RNN) for parsing scene images. MPF-RNN can enhance the capability of RNNs in modeling long-range context information at multiple levels and better distinguish pixels that are easy to confuse. Different from feedforward CNNs and RNNs with only single feedback, MPFRNN propagat...

The paper proposed a new recurrent neural network (RNN) model for systems identification and states estimation of nonlinear plants. The proposed RNN identifier is implemented in direct and indirect adaptive control schemes, incorporating a noise rejecting plant output filter and recurrent neural or linear-sliding mode controllers. For sake of comparison, the RNN model is learned both by the bac...

The process of designing neural architectures requires expert knowledge and extensive trial and error. While automated architecture search may simplify these requirements, the recurrent neural network (RNN) architectures generated by existing methods are limited in both flexibility and components. We propose a domain-specific language (DSL) for use in automated architecture search which can pro...