We study the problem of (learning) algorithm comparison, where the goal is to find differences between models trained with two different learning algorithms. We begin by formalizing this goal as one of finding disting...
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Generalization analyses of deep learning typically assume that the training converges to a fixed point. But, recent results indicate that in practice, the weights of deep neural networks optimized with stochastic grad...
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We present our experimental and theoretical framework which combines a broadband superluminescent diode (SLD/SLED) with fast learning algorithms to provide speed and accuracy improvements for the optimization of 1D op...
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Parameter tuning for robotic systems is a time-consuming and challenging task that often relies on domain expertise of the human operator. Moreover, existing learning methods are not well suited for parameter tuning f...
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A variety of supervise learning methods have been proposed for low-dose computed tomography (CT) sinogram domain denoising. Traditional measures of image quality have been employed to optimize and evaluate these metho...
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Agents’ learning from feedback shapes economic outcomes, and many economic decision-makers today employ learning algorithms to make consequential choices. This note shows that a widely used learning algorithm—Ε-Gre...
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Machine learning(ML) makes machines independent and self-learning component. Researchers applying machine learning algorithms to solve various real word problems in various domains. Nowadays agriculture affects by var...
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Merchant ship-radiated noise, recorded on a single receiver in the 360-1100 Hz frequency band over 20 min, is employed for seabed classification using an ensemble of deep learning (DL) algorithms. Five different convo...
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Merchant ship-radiated noise, recorded on a single receiver in the 360-1100 Hz frequency band over 20 min, is employed for seabed classification using an ensemble of deep learning (DL) algorithms. Five different convolutional neural network architectures and one residual neural network are trained on synthetic data generated using 34 seabed types, which span from soft-muddy to hard-sandy environments. The accuracy of all of the networks using fivefold cross-validation was above 97%. Furthermore, the impact of the sound speed and water depth mismatch on the predictions is evaluated using five simulated test cases, where the deeper and more complex architectures proved to be more robust against this variability. In addition, to assess the generalizability performance of the ensemble DL, the networks were tested on data measured on three vertical line arrays in the Seabed Characterization Experiment in 2017, where 94% of the predictions indicated that mud over sand environments inferred in previous geoacoustic inversions for the same area were the most likely sediments. This work presents evidence that the ensemble of DL algorithms has learned how the signature of the sediments is encoded in the ship-radiated noise, providing a unified classification result when tested on data collected at-sea.
Based on the VGG19-fully convolutional network (FCN) (VGG19-FCN) and U-Net model in the deep learning algorithms, the left ventricle in the ultrasonic cardiogram was segmented automatically. In addition, this study ev...
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Based on the VGG19-fully convolutional network (FCN) (VGG19-FCN) and U-Net model in the deep learning algorithms, the left ventricle in the ultrasonic cardiogram was segmented automatically. In addition, this study evaluated the value of ultrasonic cardiogram features after segmentation by the optimized algorithm in diagnosing patients with coronary heart disease (CHD) and angina pectorisody;patients with arrhythmia;and pa. In this study, 30 patients with confirmed CHD and 30 normal people without CHD from the same hospital in a certain area were selected as the research objects. Firstly, the VGG19-FCN and U-Net model algorithms were selected to automatically segment the left ventricular part of the apical four-chamber static image, which was realized through the weights of the fine-tune basic model algorithm. Subsequently, the experimental subjects were divided into a normal group and a CHD group, and the data were obtained through the ultrasonic cardiogram feature analysis of automatic segmentation by the algorithm. The differences in the ejection fraction (EF), left ventricular fractional shortening (FS), and E/A values (in early and late of the diastolic phase) of the left ventricle for patients in the two groups were compared. In addition, the ultrasonic cardiogram left ventricular segmentation results of normal people and patients with CHD were compared. A comprehensive analysis suggested that the U-Net model was more suitable for the practical application of automatic ultrasonic cardiogram segmentation. According to the analyzed data results, the global systolic function parameters (EF, FS, and E/A values) of the left ventricle for patients showed statistically obvious differences (P < 0.05). In summary, deep learning algorithms can effectively improve the efficiency of ultrasonic cardiogram left ventricular segmentation, show a great role in the diagnosis of CHD patients, and provide a reliable theoretical basis and foundation research on the subsequent CHD
Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing algorithms. Enabling the design of datasets to test specific properties and failure mo...
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ISBN:
(纸本)9781713829546
Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing algorithms. Enabling the design of datasets to test specific properties and failure modes of learning algorithms is thus a problem of high interest, as it has a direct impact on innovation in the field. In this sense, we introduce Synbols - Synthetic Symbols - a tool for rapidly generating new datasets with a rich composition of latent features rendered in low resolution images. Synbols leverages the large amount of symbols available in the Unicode standard and the wide range of artistic font provided by the open font community. Our tool's high-level interface provides a language for rapidly generating new distributions on the latent features, including various types of textures and occlusions. To showcase the versatility of Synbols, we use it to dissect the limitations and flaws in standard learning algorithms in various learning setups including supervised learning, active learning, out of distribution generalization, unsupervised representation learning, and object counting.
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