在 中文 中使用 随机噪声 的示例及其翻译为 英语
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随机噪声vn(t)具有高斯(正态)分布。
因此，所有50个解释变量也是随机噪声。
So, all 50 explanatory variables are random noise as well.在每一点上，信号都被随机噪声污染。
In each of the points, the signal has been contaminated by random noise.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。
Again, we are regressing random noise, there is absolutely no relationship in it, but still we find a significant model with 7 significant parameters.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）。
Random noise can be a decent regulariser, it can even improve performance in some settings(this technique is called label smoothing or soft labels).Combinations with other parts of speech
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。
From some starting point(e.g. random noise, or the content image itself), the pastiche image is progressively refined until these requirements are met.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。
This process can prevent random noise or other disturbance signal awakens the“key”, extend battery life.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。
Also, we can add some random noise in correlated variable so that the variables become different from each other.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）。
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.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。
Even the best test andmeasurement instruments can possess manufacturing imperfections, random noise, and long-term drift that can cause measurement errors.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。
But as the number of values increases,the probability associated with each value gets smaller and the effect of random noise increases.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。
We design a system, based on deep learning,for generating unique but realistic sequences from 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。
Okay, so,you are saying we can easily fool a network by adding 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。
This phenomenon is qualitatively unaffected by explicit regularization,and occurs even if we replace the true images by completely unstructured 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。
As it happens, I don't like that term-the only truly unstructured data is random noise.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。
This data is thenprocessed to remove various types of coherent and random noise, leaving just the primary signal of interest.问题是，在竞争战略决策方面往往有很多“随机噪声”。
The problem is that there's plenty of random noise in competitive strategic decisions.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。
To augment the sound attenuation,and ensure further enhanced security, random noise is produced inside the box once it is closed and activated.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。
Also, we can add some random noise in correlated variable so that the variables become different from each other.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。
If you have an unconnected analog pin,it might pick up random noise from the surrounding environment.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。
Use non-conventional train/test splits and add random noise to evaluate your generalization power.DC-to-DCConverterCombatsEMI：图2表示随机噪声发生器通过随机转换频率降低EMI。
DC-to-DC Converter Combats EMI:Figure 2 shows how a random noise generator randomly shifts the frequency to reduce EMI.有了这样一条公式，研究者就能将有意义的信号和随机噪声区分开来了。
Such a formula allows researchers to differentiate meaningful signals from random noise.图像噪声在本质上是随机噪声。
如果使用不当，它可以简单地放大随机噪声。
Used improperly, it can simply amplify random noise.另一个重要的随机噪声来源是热噪声。
The other main noise source is read noise.FEC（前向纠错）的使用抑制了长距离链路的随机噪声。
Use of Forward Error Correction(FEC) limits the impact of random noise on long-distance links.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。
Random noise in digital images is manifested by spurious pixels having unusually high or low intensity values.让我们首先向图像加入一些随机噪声，并且确保噪声非常接近于0。
Let's start adding random noise to the image and keep it very close to zero.对于这种情况，4LSb(最低有效位)表示ADC中生成的随机噪声。
In this case, the 4 LSb's(Least Significant Bit)represent random noise produced in the ADC.
