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The attractor-based complexity of a Boolean neural network is a measure which refers to the ability of the network to perform more or less complicated classification tasks of its inputs via the manifestation of meaningful or spurious attractor dynamics. Here, we study the attractor-based complexity of a Boolean model of the basal ganglia-thalamocortical network. We show that the regulation of the...
Time series prediction relies on past data points to make robust predictions. The span of past data points is important for some applications since prediction will not be possible unless the minimal timespan of the data points is available. This is a problem for cyclone wind-intensity prediction, where prediction needs to be made as a cyclone is identified. This paper presents an empirical study on...
Traditional convolutional layers extract features from patches of data by applying a non-linearity on an affine function of the input. We propose a model that enhances this feature extraction process for the case of sequential data, by feeding patches of the data into a recurrent neural network and using the outputs or hidden states of the recurrent units to compute the extracted features. By doing...
By the fundamental neural filtering theorem, a properly trained recursive neural filter with fixed weights that processes only the measurement process generates recursively the conditional expectation of the signal process with respect to the joint probability distributions of the signal and measurement processes and any uncertain environmental process involved. This means that a recursive neural...
Sentiment classification has been a very hot topic in the field of natural language processing (NLP) and understanding in recent years. Recurrent neural networks (RNN) is a widely used tool to deal with the classification problem of variable-length sentences. The standard RNN can only access the preceding context of a sentence. In this paper, a new architecture termed Comprehensive Attention Recurrent...
We present a programmable high-efficient Recurrent Neural Network (RNN) with Synapses design using Resistive Random Access Memory (ReRAM). The presented ReRAM-RNN employs crossbar ReRAM arrays as synapses. A fast synapses programming methodology is realized by CMOS-based neuron with in-built programming circuitry. The simulations are performed using experimentally verified physical resistive switching...
Visual context is fundamental to understand human actions in videos. However, to efficiently employ temporal context information presents an enormous challenge to this area. Two main problems are long-standing: (1) video frames are redundant while discriminative information is sparse; (2) large amount of interference information is mixed in frame sequences. These factors results in redundant computation...
EEG is one the most effective tools used in the diagnosis of epilepsy. However, proper diagnosis of epilepsy requires the detection and analysis of epileptic seizures for a long period of time. Manual monitoring of long term EEG is tedious and costly. Therefore, a reliable automated seizure detection system is desirable. Most current state-of-the-art methods use hand crafted feature extraction and...
The hardware implementation of neural network models allows to efficiently exploit their inherent parallelism. Here, we focus on the Liquid State Machine (LSM) methodology to build recurrent Spiking Neural Networks (SNN), particularly suited to process time-dependent signals. We propose a low cost hardware implementation of LSM networks based on the use of stochastic computing (SC) concepts. The functionality...
Recurrent Neural Networks (RNNs), which are a powerful scheme for modeling temporal and sequential data need to capture long-term dependencies on datasets and represent them in hidden layers with a powerful model to capture more information from inputs. For modeling long-term dependencies in a dataset, the gating mechanism concept can help RNNs remember and forget previous information. Representing...
In this paper, we analyze the performance of various sequence to sequence neural networks on the task of grapheme to phoneme (G2P) conversion. G2P is a very important component in applications like text-to-speech, automatic speech recognition etc,. Because the number of graphemes that a word consists of and the corresponding number of phonemes are different, they are first aligned and then mapped...
The capacity of recurrent neural networks to learn complex sequential patterns is improving. Recent developments such as Clockwork RNN, Stack RNN, Memory networks and Neural Turing Machine all aim to increase long-term memory capacity of recurrent neural networks. In this study, we investigate properties of Neural Turing Machine, compare it with ensembles of Stack RNN on artificial benchmarks and...
Reservoir Computing Network (RCN) is a special type of the single layer recurrent neural networks, in which the input and the recurrent connections are randomly generated and only the output weights are trained. Besides the ability to process temporal information, the key points of RCN are easy training and robustness against noise. Recently, we introduced a simple strategy to tune the parameters...
Cyclone track prediction is a two dimensional time series prediction problem that involves latitudes and longitudes which define the position of a cyclone. Recurrent neural networks have been suitable for time series prediction due to their architectural properties in modeling temporal sequences. Coevolutionary recurrent neural networks have been used for time series prediction and also applied to...
Recently, models of neural networks in the real domain have been extended into the high dimensional domain such as the complex number and quaternion domain, and several high-dimensional models have been proposed. These extensions are generalized by introducing Clifford algebra (geometric algebra). In this paper we extend conventional real-valued models of recurrent neural networks into the octonion...
This work is concerned with the existence and uniqueness of pseudo almost-periodic solution for a class of impulsive recurrent neural network networks with mixed delays. Some criteria are established to prove the asymptotic stability of the equilibrium point. Tow illustrative an example with numerical simulations are given to show the validity of the main results.
The hash symbol, called a hashtag, is used to mark the keyword or topic in a tweet. It was created organically by users as a way to categorize messages. Hashtags also provide valuable information for many research applications such as sentiment classification and topic analysis. However, only a small number of tweets are manually annotated. Therefore, an automatic hashtag recommendation method is...
Statistical models built on historical data are often found to be effective in forecasting Indian summer monsoon. However, linear models are found to be inadequate, and non-linear models like neural networks provide better performance. In this article, we study the use of recurrent neural network for long range forecast of Indian monsoon at lead of one season. Recurrent network model the sequential...
Image segmentation refers to the process of dividing an image into multiple regions which represent meaningful areas. Image segmentation is an essential step for most image analysis tasks such as object recognition and tracking, pattern recognition, content-based image retrieval, etc. In recent years, a large number of image segmentation algorithms have been developed, but achieving accurate segmentation...
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