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Chinese traditional visual culture symbols (CT-VCSs) is formed in the tradition and has the characteristic of Chinese unique ideological and cultural connotation. It is a visual cultural heritage of Chinese culture. So the research on CT-VCSs has important practical significance. In this paper, it is mainly about the recognition and classification of CT-VCSs based on machine learning. We make use...
Support vector machine has good generalization ability to the small sample problem, and it has become a hot spot in domestic and foreign scholars in recent years. The support vector machine based on the radius of the support vector machine and the twin support vector machine are extensions of the support vector machine, which has better performance. The main research of the thesis is combined with...
To explore the potential of training complex deep neural networks (DNNs) on other commercial chips rather than GPUs, we report our work on swDNN, which is a highly-efficient library for accelerating deep learning applications on the newly announced world-leading supercomputer, Sunway TaihuLight. Targeting SW26010 processor, we derive a performance model that guides us in the process of identifying...
Reliable traffic light detection and classification is crucial for automated driving in urban environments. Currently, there are no systems that can reliably perceive traffic lights in real-time, without map-based information, and in sufficient distances needed for smooth urban driving. We propose a complete system consisting of a traffic light detector, tracker, and classifier based on deep learning,...
In many gas sensors, the selectivity is still a big issue, which makes the real concentration distortion. We introduce a way to estimate its concentration in gas mixture condition. The system consists of three gas sensors, carbon dioxide sensor, carbon monoxide sensor and humidity sensor in environment-controlled chamber, which give lots of different concentration combinations. To estimate its concentration,...
Kernel matrices appear in machine learning and non-parametric statistics. Given N points in d dimensions and a kernel function that requires O(d) work to evaluate, we present an O(dN log N)-work algorithm for the approximate factorization of a regularized kernel matrix, a common computational bottleneck in the training phase of a learning task. With this factorization, solving a linear system with...
Nowadays, developing effective techniques able to deal with data coming from structured domains is becoming crucial. In this context kernel methods are the state-of-the-art tool widely adopted in real-world applications that involve learning on structured data. Contrarily, when one has to deal with unstructured domains, deep learning methods represent a competitive, or even better, choice. In this...
Because sparse matrix-vector multiplication (SpMV) is an important and widely used computational kernel in many real-world applications, it behooves us to accelerate SpMV on modern multi- and many-core architectures. While many storage formats have been developed to facilitate SpMV operations, the compressed sparse row (CSR) format is still the most popular and general storage format. However, parallelizing...
This paper proposes a novel framework for non-blind de-convolution using deep convolutional network. To deal with various blur kernels, we reduce the training complexity using Wiener filter as a preprocessing step in our framework. This step generates amplified noise and ringing artifacts, but the artifacts are little correlated with the shapes of blur kernels, making the input of our network independent...
Increasing architectural diversity makes performance portability extremely important for parallel simulation codes. Emerging on-node parallelization frameworks such as Kokkos and RAJA decouple the work done in kernels from the parallelization mechanism, allowing for a single source kernel to be tuned for different architectures at compile time. However, computational demands in production applications...
Natural Language Inference (NLI) is a key, complex task where machine learning (ML) is playing an important role. However, ML has progressively obfuscated the role of linguistically-motivated inference rules, which should be the core of NLI systems. In this paper, we introduce distributed inference rules as a novel way to encode linguistically-motivated inference rules in learning interpretable NLI...
Convolutional Neural Networks (CNNs) have proven effective for machine learning tasks such as computer vision. Analog, asynchronous hardware implementations of such neural networks appear to be promising avenues for fast, online, real-time, energy efficient machine learning. However, the weight-sharing requirements of CNNs present challenges for such neuromorphic designs. We propose a biologically...
Recent work demonstrated the value of multi clusters of spiking Random Neural Networks (RNN) with dense soma-to-soma interactions in deep learning. In this paper we go back to the original simpler structure and we investigate the power of single RNN cells for deep learning. First, we consider three approaches with the single cells, twin cells and multi-cell clusters. This first part shows that RNNs...
Principal component analysis (PCA) and kernel PCA (KPCA) are the state-of-art machine learning methods widely used in industrial process monitoring and fault detection field. However, these methods build shallow statistical models based on single layer of features and may not achieve the best monitoring performance. In order to sufficiently mine the intrinsic data features, a deep learning based nonlinear...
Machine learning based network anomaly detection methods, which are already effective defense mechanisms against known network intrusion attacks, have also proven themselves to be more successful on the detection of zero-day attacks compared to other types of detection methods. Therefore, research on network anomaly detection using deep learning is getting more attention constantly. In this study...
Thanks to modern deep learning frameworks that exploit GPUs, convolutional neural networks (CNNs) have been greatly successful in visual recognition tasks. In this paper, we analyze the GPU performance characteristics of five popular deep learning frameworks: Caffe, CNTK, TensorFlow, Theano, and Torch in the perspective of a representative CNN model, AlexNet. Based on the characteristics obtained,...
Kernel function implicitly maps data from its original space to a higher dimensional feature space. Kernel based machine learning algorithms are typically applied to data that is not linearly separable in its original space. Although kernel methods are among the most elegant part of machine learning, it is challenging for users to define or select a proper kernel function with optimized parameter...
This paper utilizes the deep learning algorithm to classify the Street View images. We did some research to find the appropriate convolutional neural network model that suits the classification of the street view images. We firstly collected our own dataset. Based on the convolutional neural network model AlexNet and according to the characteristics the dataset mentioned above to adjust the model...
This paper investigates a new approach of finding sentence level sentiment analysis using different machine learning algorithms. Three different machine learning algorithms — SVM (Support Vector Machine), Naïve Bayes and MLP (Multilayer Layer Perceptron) have been used both for sentiment analysis. Moreover two different classifiers of Naïve Bayes and two different types of SVM kernels have been used...
Recent years the number of vehicles increases tremendously. Because of that to identify the vehicle is significant task. Vehicle color and number plate recognition are various ways to identify the vehicle. So Vehicle color recognition essential part of an intelligent transportation system. There are several methods for recognizing the color of the vehicle like feature extract, template matching, convolutional...
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