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15823_chat
Window下的select方式网络程序的范例(Window select the mode network programming paradigm)
- 2005-02-22 13:40:52下载
- 积分：1
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myfft
fft自编源码，用于快速傅立叶变换的matlab程序(fft own source for the Fast Fourier Transform matlab procedures)
- 2008-06-27 17:46:35下载
- 积分：1
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SOA
用MATLAB编程实现半导体光放大器（SOA）的模拟(MATLAB programming realize semiconductor optical amplifiers (SOA) simulation
)
- 2012-05-13 09:59:07下载
- 积分：1
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pybblyzf
利用平移不变量发，实现小波阈值去噪，能够有效的消去伪吉布斯现象，利于后续的特征点提取。(The use of translation invariant issued to wavelet threshold denoising can effectively eliminate the Gibbs phenomenon, and conducive to the subsequent feature point extraction.)
- 2012-03-29 19:44:37下载
- 积分：1
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ICA_PICTURE
应用独立分量分析方法对图片进行图像分离处理，可以将医学影像的图片进行分离，单独进行分析。(Independent component analysis of images for image separation processing, medical imaging picture can be separated, analyzed separately.)
- 2015-03-16 10:27:15下载
- 积分：1
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Wind-and-joint-scheduling
风水火联合调度:建立兼顾联合运行系统经济效益最大和弃风电量最小的风-水-火联合运行系统多目标优化调度模型，基于优先配置风蓄联合发电系统，再优化常规火电机组出力的思路，综合考虑了风电不确定性对抽水蓄能机组的影响、风蓄联合运行系统对常规火电机组的影响以及多台常规火电机组的机组组合问题。(Wind and joint scheduling)
- 2021-03-22 09:59:16下载
- 积分：1
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C_Primer_PlusNote
含有C_Primer_Plus_(第五版)_中文版另外还有C_Primer_Plus的读书笔记(Containing C_Primer_Plus_ (Fifth Edition) _ Chinese version Another C_Primer_Plus reading notes)
- 2014-10-27 21:50:52下载
- 积分：1
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ReportDecisionDeck-DEIM-URV
Based on the rough set theory and its reduction methods, a MATLAB
interface is written and included in the RSDA Toolbox
- 2013-12-26 23:46:51下载
- 积分：1
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FRM
频率响应屏蔽技术（FRM，Frequency Response Masking）技术实现窄带滤波器(Frequency Response Masking technology to realize narrow band filter)
- 2018-04-09 20:39:42下载
- 积分：1
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1807.01622
深度神经网络在函数近似中表现优越，然而需要从头开始训练。另一方面，贝叶斯方法，像高斯过程（GPs），可以利用利用先验知识在测试阶段进行快速推理。然而，高斯过程的计算量很大，也很难设计出合适的先验。本篇论文中我们提出了一种神经模型，条件神经过程（CNPs），可以结合这两者的优点。CNPs受灵活的随机过程的启发，比如GPs，但是结构是神经网络，并且通过梯度下降训练。CNPs通过很少的数据训练后就可以进行准确的预测，然后扩展到复杂函数和大数据集。我们证明了这个方法在一些典型的机器学习任务上面的的表现和功能，比如回归，分类和图像补全(Deep neural networks perform well in function approximation, but they need to be trained from scratch. On the other hand, Bayesian methods, such as Gauss Process (GPs), can make use of prior knowledge to conduct rapid reasoning in the testing stage. However, the calculation of Gauss process is very heavy, and it is difficult to design a suitable priori. In this paper, we propose a neural model, conditional neural processes (CNPs), which can combine the advantages of both. CNPs are inspired by flexible stochastic processes, such as GPs, but are structured as neural networks and trained by gradient descent. CNPs can predict accurately with very little data training, and then extend to complex functions and large data sets. We demonstrate the performance and functions of this method on some typical machine learning tasks, such as regression, classification and image completion.)
- 2020-06-23 22:20:02下载
- 积分：1
