The Gaussian distribution or the normal distribution, they're two words for the same thing.

高斯分布或正态分布，其实是同一概念两种表述。

Key words, gaussian elimination and row echelon form.

关键词 高斯消去和行阶梯形。

Thing, e to the minus quadratic form, is Gaussian.

, e 二次形式, 是高斯。

Whereas, gaussian lives between minus infinity and infinity.

而高斯介于无穷大和无穷大之间。

Gaussian elimination, for example, will always be faster.

高斯消元法，例如，总是会更快。

Realize  that means we could have chosen a rectangular box as our imaginary, closed Gaussian surface.

意识这意味着我们可以选择一个长方体盒子作为我们假想封闭高斯面。

And once you've done that with the technology of Gaussian splatting, you can manipulate that that scenario.

一旦你使用高斯爆破术完成了这一步，你就可以操纵那个场景。

Along comes Gaussian splatting, which is like, hey, do we even need the neural part of radiance fields?

高斯点绘制来了， 这就像在说，嘿， 我们真需要辐射场中神经部分吗？

And where this is coming from, is they're all sampled from a bell curve known as a Gaussian distribution.

而这都来自于一个被称为高斯分布钟形曲线采样。

Anytime you ever see a distribution and it's e to the minus, some quadratic form, that's Gaussian distribution.

任何时候你看一个分布， 它是 e ，一些二次形式， 那就是高斯分布。

The relevant  search term here would be gaussian curvature.

相关搜索术语应该是高斯曲率。

Statistical speech models and a probabilistic technique called Gaussian mixture modelling are then used to identify each phoneme, before reconstructing the original word.

然后由语音统计模型和一种称为高斯混合模型概率工具来识别每个语素，再将其组合为原来单词。

But basically between nerfs and Gaussian splatting, think of Gaussian splatting basically as radiance fields without that neural rendering part.

但在nerf与高斯溅射之间，可以将高斯溅射基本视为没有神经渲染部分辐射场。

To see why, we need to understand how numbers factor inside the Gaussian integers.

要明白这一点，我们需要了解数字在高斯整数中是如何分解。

Okay, just like we did with Gaussian surfaces, we need to look at the different sides of the Amperian rectangle.

好， 就像我们处理高斯面一样，我们需要观察安培矩形不同侧面。

The team says Jiuzhang's quantum computing system can implement large-scale " Gaussian boson sampling" 100 trillion times faster than the world's fastest existing supercomputer.

研究团队表示，“九章”量子计算系统可以实现大规模“高斯玻色采样”，比目前世界上最快超级计算机快100万亿倍。

Their study used a classical simulation algorithm called Gaussian boson sampling to provide a highly efficient way of demonstrating such speedup in solving some well-defined tasks.

他们研究使用了一种名为高斯玻色子采样经典模拟算法，在解决一些特定任务时提供了一种高效方式来证明这种加速。

Also, from a purely artistic standpoint, the ultimate result is just really pretty to think about, much more so that Gaussian elimination.

另外， 仅从艺术角度来看，最终结果真非常漂亮，比高斯消元法要美得多。

Now that  we have that figured out, Bobby, what shape shall we choose for our imaginary Gaussian surface?

现在我们搞清楚了， 鲍比，我们选择什么形状作为假想高斯面呢？

And, realize, while the Gaussian  surfaces we usually work with are imaginary, the Gaussian surface could  actually be a real, physical surface.

而且，要意识，虽然我们通常使用高斯面是假想，但高斯面实际上也可以是一个真实理表面。