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The paper evaluates three variants of the Gated Recurrent Unit (GRU) in recurrent neural networks (RNNs) by retaining the structure and systematically reducing parameters in the update and reset gates. We evaluate the three variant GRU models on MNIST and IMDB datasets and show that these GRU-RNN variant models perform as well as the original GRU RNN model while reducing the computational expense...
Studies in visual perceptual learning investigate the way human performance improves with practice, in the context of relatively simple (and therefore more manageable) visual tasks. Building on the powerful tools currently available for the training of Convolution Neural Networks (CNN), networks whose original architecture was inspired by the visual system, we revisited some of the open computational...
In recent years, Discriminative Correlation Filter (DCF) based methods have significantly advanced the state-of-the-art in tracking. However, in the pursuit of ever increasing tracking performance, their characteristic speed and real-time capability have gradually faded. Further, the increasingly complex models, with massive number of trainable parameters, have introduced the risk of severe over-fitting...
Nowadays a huge volume of biomedical data (images, genes, etc) are daily generated. The interpretation of such data involves a considerable expertise. The misinterpretation and/or misdetection of a suspicious clinical finding leads to increasing the negligence claims, and redundant procedures (e.g. biopsies). The analysis of biomedical data is a complex task which are performed by specialists on whose...
The need for energy-awareness in current data centers has encouraged the use of power modeling to estimate their power consumption. However, existing models present noticeable limitations, which make them application-dependent, platform-dependent, inaccurate, or computationally complex. In this paper, we propose a platform-and application-agnostic methodology for full-system power modeling in heterogeneous...
With the rapid development of biology, its relevant literature will be more and more important for researchers to dig out more valuable knowledge. Named Entity Recognition (NER) for biology literature is a very common and important task in these works. With the increase of the literature amount, the recognition speed becomes slower and less accurate. In order to tackle this problem, a MapReduce based...
Recently, more neuroscience researches focus on the role of dendritic structure during the neural computation. Inspired by the specified topologies of numerous dendritic trees, we proposed a single neural model with a particular dendritic structure. The dendrites are composed of several branches, and these branches correspond to three distributions in coordinate, which are used to classify the training...
In this work we provide details on a new and effective approach able to generate Gaussian Mixture Models (GMMs) for the classification of aggregated time series. More specifically, our procedure can be applied to time series that are aggregated together by adding their features. The procedure takes advantage of the additive property of the Gaussians that complies with the additive property of the...
Ocular biometrics in the visible spectrum has emerged as an area of significant research activity. In this paper, we propose two convolution-based models for verifying a pair of periocular images containing the iris, and compare the two approaches amongst each other as well as with a baseline model. In the first approach, we perform deep learning in an unsupervised manner using a stacked convolutional...
We live in the era of big data with dataset sizes growing steadily over the past decades. In addition, obtaining expert labels for all the instances is time-consuming and in many cases may not even be possible. This necessitates the development of advanced semi-supervised models that can learn from both labeled and unlabeled data points and also scale at worst linearly with the number of examples...
This paper presents a structural organization of declarative memories using a new model of spiking neurons. Using this model we propose a self-organizing mechanism to build episodic and semantic memories on the cognitive level. Neurons in this approach represent symbolic concepts that are stored and associated with each other based on the observed events in the environment. We demonstrate that the...
In this paper, we propose a supervised learning based model for ocular biometrics. Using Speeded-Up Robust Features (SURF) for detecting local features of the eye region, we create a local feature descriptor vector of each image. We cluster these feature vectors, representing an image as a normalized histogram of membership to various clusters, thereby creating a bag-of-visual-words model. We conduct...
Recognition of traffic signs is vary important in many applications such as in self-driving car/driverless car, traffic mapping and traffic surveillance. Recently, deep learning models demonstrated prominent representation capacity, and achieved outstanding performance in traffic sign recognition. In this paper, we propose a traffic sign recognition system by applying convolutional neural network...
In the context of medical team leaders training, we present a multiagent communication model that can introduce errors in a team of agents. This model is built from existing work from the literature in multiagents systems and information science, but also from a corpus of dialogues collected during actual field training for medical teams. Our model supports four types of communication errors (misunderstanding,...
Ridge regression is an important algorithm in machine learning and has been widely used in real-world applications like recommendation systems. Trained with a large training dataset, it outputs a curve that models the relationship between a scalar dependent variable and one or more explanatory variables. In general, the more training dataset it is fed, the more accurate the resulting model will be...
Deep convolutional neural networks (CNN) brought revolution without any doubt to various challenging tasks, mainly in computer vision. However, their model designing still requires attention to reduce number of learnable parameters, with no meaningful reduction in performance. In this paper we investigate to what extend CNN may take advantage of pyramid structure typical of biological neurons. A generalized...
Gait recognition aims to identify people through the analysis of the way they walk. The challenge of model-free based gait recognition is to cope with various intra-class variations such as clothing variations and carrying conditions that adversely affect the recognition performances. This paper proposes a novel method which combines Statistical Dependency (SD) feature selection with Globality-Locality...
Hierarchical Model and X (HMAX) is an outstanding bio-inspired model for object recognition. But there are still some limitations such as its poor invariance to rotation and processing speed. To lessen these limitations and improve the model's performance, we extend the HMAX model and propose a novel representation method by combining HMAX with Histogram of Oriented Gradients (HOG), denoted as HOG-HMAX...
The paper presents selected results of research on modeling and modeling identification nerone's development of the electricity market. For example on the electricity market shows the results of neural and identification modeling. As a result of comparative studies showing that yields better results neural modelling than identification modelling for this type of systems containing a large number of...
We propose in this paper a deep learning model based on convolutional neural network (CNN) for biopsy image grading. The model outputs a vector of scores indicating presence or severity of the target histopathological characteristics. Within the model, we first design a 7-layer CNN for feature representation and high level concept extraction. Each biopsy image is expressed as a feature vector through...
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