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CO_2
说明： 激光测速研究，主要介绍多普勒激光测速知识(Laser velocimetry study introduces laser-Doppler velocimetry knowledge)
- 2008-10-24 22:29:44下载
- 积分：1
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kDISTRUBUTIONPcfaR
k分布复海杂波条件下的目标cfa检测程序(kDISTRUBUTION cfaR)
- 2013-12-12 10:13:25下载
- 积分：1
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GSRC_ECCV
this package contains functions of image gabor fatures extractrion.
- 2016-05-30 18:04:18下载
- 积分：1
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grayrlmatrix
gray level run length martix
- 2009-05-19 18:01:16下载
- 积分：1
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RMSE
说明： 通过matlab编写的几个关于PSNR、mse的图像处理的小程序，希望能对大家有所帮助。(root-mean-square error )
- 2011-04-12 22:38:18下载
- 积分：1
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quad2dggen
this s very useful,quad2dggen.rar
- 2010-05-20 14:28:29下载
- 积分：1
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wjxptztq
硕士学位论文基于无线传感器网络的水下目标识别中的线谱特征提取，(Master' s degree thesis on wireless sensor networks for underwater target recognition in the line spectrum feature extraction,)
- 2011-09-13 21:18:14下载
- 积分：1
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GREY-MODEL
灰色GM（1,1）预测模型，希望有用，大家一起学习学习。望大家能有所提高(Gray GM (1,1) forecasting model, hope useful, we learn together. Hope we can be improved)
- 2014-12-05 10:15:52下载
- 积分：1
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fd-psola
基于频域基音同步叠加的语音合成算法，在时域基音同步叠加的基础上增加频域调整的部分(Speech synthesis algorithm based on frequency domain pitch synchronous overlap and increase the frequency domain adjusted on the basis of the time-domain pitch synchronous superimposed on part)
- 2021-03-02 21:19:33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
