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

高斯分布或正态分布，其实是同概念的两种表述。

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.

事物, e 到负二次形式, 是高斯。

Whereas, gaussian lives between minus infinity and infinity.

而高斯介于负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.

意识到意味着我们可以选择个长方体盒子作为我们假想的封闭高斯面。

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

旦高斯爆破术完成了，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， 就像在说，嘿， 我们真的需要辐射场中的神经部分吗？

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

而都来自于个被称为高斯分布的钟形曲线采样。

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

任何时候看到个分布， 它是 e 到负的，些二次形式， 那就是高斯分布。

The relevant  search term here would be gaussian curvature.

相关的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种称为高斯混合模型的概率工具来识别每个语素，再将其组合为原来的单词。

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的辐射场。

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

要明白点，我们需要了解数字在高斯整数中是如何分解的。

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

好的， 就像我们处理高斯面样，我们需要观察安培矩形的不同侧面。

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的“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.

他们的研究了种名为高斯玻色子采样的经典模拟算法，在解决些特定任务时提供了种高效的方式来证明种加速。

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，最终结果真的非常漂亮，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的高斯面呢？

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

而且，要意识到，虽然我们通常的高斯面是假想的，但高斯面实际上也可以是个真实的物理表面。