Several segmentation techniques have been proposed, from these Deep Learning techniques have yielded promising results. This work presents a comparison between four state of the art (SOTA) Deep Learning segmentation algorithms (UNet, SUMNet, ResUNet and Attention UNet). Each network was trained on ...
Algorithms for Semantic Segmentation of Multispectral Remote Sensing Imagery using Deep Learning Deep convolutional neural networks (DCNNs) have been used to achieve\nstate-of-the-art performance on many computer vision tasks (e.g., object\nrecognition, object detection, semantic segmentation) thanks ...
吴恩达的Improving Deep Neural Networks课程的第二节对momentum方法有比较细致的描述,momentum被理解为一种对之前步骤中计算的梯度的加权平均,且越早的步的权重越小(指数递减)。 Exponentially weighted average 方法对自变量接近0处的函数值的预测不准,因此往往需要使用bias correction项来弥补这一点。Adam算法中也使用了...
Third, any preventable costs, defined as costs of preventable hospitalizations and preventable ED visits within 1 year from 1/1/2017, which is a commonly used cost horizon. To identify potentially preventable ED visits, we used a combination of two validated algorithms. The first was an updated...
In this paper, the efficiency of five Machine Learning (ML) methods consisting of Deep Learning (DL), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Gradient Tree Booting (GTB) for regression and classification of the Ultimate Load Factor (ULF) of nonlinear inela...
Deep learning algorithms for discriminant autoencoding In this paper, a new family of Autoencoders (AE) for dimensionality reduction as well as class discrimination is proposed, using various class separating m... P Nousi,A Tefas - 《Neurocomputing》 被引量: 8发表: 2017年 On the Brittleness...
Deep learning is an algorithmic model with a more complicated structure. This makes it possible to evaluate as many aspects of the problem as possible. Because of this, deep learning algorithms have become a very important modeling tool for traffic engineering issues in recent years (Ma et al....
The aim of this study is to compare the utility of several supervised machine learning (ML) algorithms for predicting clinical events in terms of their internal validity and accuracy. The results, which were obtained using two statistical software platforms, were also compared. Materials and methods...
Comparing human-centric and robot-centric sampling for robot deep learning from demonstrations Motivated by recent advances in Deep Learning for robot control, this paper considers two learning algorithms in terms of how they acquire demonstrations f... M Laskey,C Chuck,J Lee,... - IEEE ...
Five discrete-frequency linear learning-control laws are compared and experimentally tested, These include simple integral-control-based learning using a single learning gain, phase-cancellation learning control, a contraction-mapping learning-control law with monotonic decay of the error norm, and leaning...