What is the translation of " RANDOM NOISE " in Chinese?

• 随机噪声
• 随机噪音

Examples of using Random noise in English and their translations into Chinese

The sounds don't seem to be coded, just random noise.
声音似乎没有编码，只是随机噪音。

The random noise vn(t) has a Gaussian(normal) distribution.
随机噪声vn(t)具有高斯(正态)分布。

So, all 50 explanatory variables are random noise as well.
因此，所有50个解释变量也是随机噪声。

This process can prevent random noise or other disturbance signal awakens the“key”, extend battery life.
这个过程能够防止随机噪声或其他干扰信号唤醒“钥匙”，延长电池寿命。

Okay, so, you are saying we can easily fool a network by adding random noise.
好了，也就是说我们可以通过添加随机噪声来轻松欺骗网络。

The problem is that there's plenty of random noise in competitive strategic decisions.
问题是，在竞争战略决策方面往往有很多“随机噪声”。

But these models are often computationally intensive and produce a lot of random noise.".
但这些模型通常是计算密集型的，会产生大量的随机噪音。

Use non-conventional train/test splits and add random noise to evaluate your generalization power.
使用非传统的训练/测试并添加随机噪声来评估模型的泛化能力。

As it happens, I don't like that term- the only truly unstructured data is random noise.
碰巧的是，我不喜欢这个词，真正的非结构化数据是随机噪声。

If you have an unconnected analog pin, it might pick up random noise from the surrounding environment.
如果你有一个未连接的模拟引脚，它可能会从周围环境中拾取随机噪声。

Information theory teaches us that compressed data is statistically similar to random noise;
信息理论告诉我们，压缩数据统计上接近于随机噪音；.

Random noise in digital images is manifested by spurious pixels having unusually high or low intensity values.
数字图像中的随机噪声表现杂散像素，具有异常高或低强度值。

We design a system, based on deep learning, for generating unique but realistic sequences from random noise.
设计一个基于深度学习的模型，用于从随机噪声中产生独特但真实的序列。

Also, we can add some random noise in correlated variable so that the variables become different from each other.
此外，我们可以添加一些随机噪声相关变量，使变量变得彼此不同。

Use of Forward Error Correction(FEC) limits the impact of random noise on long-distance links.
FEC（ForwardErrorCorrection，前向纠错）的使用抑制了长距离链路的随机噪音。

Also, we can add some random noise in correlated variable so that the variables become different from each other.
我们还可以在相关变量里添加一些随机噪声，使得变量变得彼此不同。

Unfortunately, sharpening convolution filters have the undesirable effect of enhancing random noise in digital images.
不幸的是，锐化卷积滤波器具有提高数字图像中的随机噪声的不良影响。

It reduces random noise that exists in raw variables- similar to averaging and yes, you lose some information here.
它减少了原始变量中存在的随机噪声-类似于平均值，是的，你在这里丢失了一些信息。

They also provide security in that the signals appear as though they were random noise to unauthorized earth stations.
它们还提供安全性，因为信号看起来好像是对未经授权的地球站的随机噪声。

Again, we are regressing random noise, there is absolutely no relationship in it, but still we find a significant model with 7 significant parameters.
再次，我们是在回归随机噪声，它绝对没有关系，但我们仍然找到一个有7个重要参数的显著模型。

Specialized convolution kernels, often termed smoothing filters, are very useful in reducing random noise in digital images.
专门的卷积核，通常被称为平滑滤波器，在降低数字图像中的随机噪声是非常有用的。

Random noise can be a decent regulariser, it can even improve performance in some settings(this technique is called label smoothing or soft labels).
随机噪声的问题比较容易解决，它可以通过某些参数设置提高性能（技术上称为标签的平滑或软标签）。

This data is then processed to remove various types of coherent and random noise, leaving just the primary signal of interest.
然后对这些数据进行处理，消除各种类型的连贯和随机噪声，只留下主要的有用信号。

As such, it reduces the risk of overfitting as well as the Rademacher complexity of the model, i.e. its ability to fit random noise.
因此，它降低了过拟合的风险以及模型的Rademacher复杂度（即适合随机噪声的能力）。

To augment the sound attenuation, and ensure further enhanced security, random noise is produced inside the box once it is closed and activated.
为了增强声音衰减并确保进一步增强安全性，一旦闭合和激活，盒子内部就会产生随机噪声。

But as the number of values increases, the probability associated with each value gets smaller and the effect of random noise increases.
但随着数据的增加，每个值的概率就会降低，而随机噪声的影响就会增大。

From some starting point(e.g. random noise, or the content image itself), the pastiche image is progressively refined until these requirements are met.
从某个起始点（如：随机噪声或内容图像本身）开始，该pastiche会逐渐细化，直到这些要求都得到满足。

As such, it reduces the risk of overfitting as well as the Rademacher complexity of the model, i.e. its ability to fit random noise.
正是如此，它减小了模型过拟合的风险，同时降低了模型的Rademacher复杂度，即拟合随机噪音的能力。

Even the best test and measurement instruments can possess manufacturing imperfections, random noise, and long-term drift that can cause measurement errors.
即使最好的测试和测量仪器也可能存在制造缺陷、随机噪声和可能导致测量误差的长期漂移。

This phenomenon is qualitatively unaffected by explicit regularization, and occurs even if we replace the true images by completely unstructured random noise.
这种现象本质上不受显式正则化影响，即使我们通过完全非结构化随机噪声来替换真实图像，也会发生这种现象。