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FLch7NNeg2
多维非线性函数辨识的MATLAB程序,用神经网络学习二维非线性函数
(Identification of multi-dimensional nonlinear function of the MATLAB program, using neural networks to learn two-dimensional nonlinear function)
- 2007-11-07 14:33:15下载
- 积分：1
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hoighcircle
应用hough变换检测圆的代码，很方便。(Hough transform circle detection application of the code, very convenient.)
- 2010-01-20 14:56:56下载
- 积分：1
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LDAMatab
用matlab编写的lde算法，实现的数据分析，抽取分类信息和压缩特征空间维数(Lde prepared using matlab algorithm to achieve the data analysis, feature extraction classified information and compressed space dimension)
- 2010-05-29 13:03:33下载
- 积分：1
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seidel
matlab中Gauss_Seidel迭代法的实现(matlab implementation of iterative methods in Gauss_Seidel)
- 2011-05-28 10:00:46下载
- 积分：1
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quyushengzhangfa
用区域生长法分割图像,该文件包括完整的代码。(segmente images in Regional growth method, include the full code
)
- 2011-01-05 19:31:14下载
- 积分：1
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guoluyeweiPID
锅炉液位控制的MATLAB仿真方案，很好很详细(Boiler Level Control of the MATLAB simulation program)
- 2010-05-13 20:01:51下载
- 积分：1
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FDJS
系统施工接地技术。适用于产品设计开发中抗干扰的处理。(grounding system construction technology. Apply to product design development interference treatment.)
- 2007-04-22 16:46:59下载
- 积分：1
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imageprocess
现代数字图像处理技术提高及应用案例详解(Matlab版)源码(Modern digital image processing technology to improve and Applications Comments (Matlab version) source)
- 2015-01-16 17:49:45下载
- 积分：1
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8765
tab bar controller 自定义颜色和演示demo，精选ios编程学习源码，很好的参考资料。(Tab bar controller custom colors and demonstration of demo, selection of learning IOS programming source code, a good reference.)
- 2013-12-08 17:14:48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
