Lakshminaraynan2016年的工作提出了一种简化的可替代贝叶斯方法的算法,也就是Deep Ensembles(DE),DE方法概念上简化了:使用不同的初始值重复训练相同的网络结构。初始值的随机性和训练过程的随机性能够产生不同的网络参数。如果网络优化目标是最小化均方误差损失(mean squared error loss),那么它只
Deep learning algorithm-enabled sediment characterization techniques to determination of water saturation for tight gas carbonate reservoirs in Bohai Bay Basin, China Xiao Hu, Qingchun Meng, Fajun Guo, Jun Xie, Eerdun Hasi, Hongmei Wang, Yuzhi Zhao, Li Wang, Ping Li, Lin Zhu, ...
Deep learning (DL) based detection models are powerful tools for large-scale analysis of dynamic biological behaviors in video data. Supervised training of a DL detection model often requires a large amount of manually-labeled training data which are time-consuming and labor-intensive to acquire. ...
In the Stanford course on deep learning for computer vision titled “CS231n: Convolutional Neural Networks for Visual Recognition” developed by Andrej Karpathy, et al., the Adam algorithm is againsuggested as the default optimization method for deep learning applications. In practice Adam is curre...
1.1. Deep learning Deep learning existed for more than three decades ago. It is another branch of machine learning that has become the leading research focus in recent years. According to Gibson and Patterson (2016), the common definition of deep learning involves a neural network that contains...
Researchers in the Stanford Machine Learning Group, led by Andrew Ng, an adjunct professor of computer science, set out to develop a deep learning algorithm to detect 13 types of arrhythmia from ECG signals and partnered with the heartbeat monitor company iRhythm to collect a massive dataset tha...
In simple terms, machine learning algorithms refer to computational techniques that can find a way to connect a set of inputs to a desired set of outputs by learning relevant data. From: Deep Learning Models for Medical Imaging, 2022
Learning). AsforthenameofDeepLearning,Hintonjoked:"Iwanttocall SVMshallowLearning."DeepLearningitselfmeansDeepLearning, becauseitassumesthattherearelayersofneuralnetworks. Inconclusion,DeepLearningisanewalgorithmworthpaying attentionto. DeepLearningisanewfieldintheMLstudy,whichisintroduced ...
In the deep learning collaborative filtering stage, similarity calculation is the most time-consuming process, so the algorithm complexity of this process is mainly analyzed. Assuming that the magnitude of the scoring matrix is denoted as m∗n, where the number of users is m and the quantity ...
Learning algorithms. Taking into account that this data can be in form of images, several ML algorithms, such as Artificial Neural Networks, Support Vector Machines, or Deep Learning Algorithms, are particularly suitable candidates to help in medical diagnosis. This works aims to study the ...