fingers crossed that someone eventually writes “CAD kernel in One Decade…”
Just encountered the bite-sized book series, "Game Physics in One Weekend" (obviously inspired by the Ray-Tracing equivalent). Looks promising, going to take a closer look! gamephysicsweekend.github.io
The Legendre-Fenchel transform extends the Legendre transform to arbitrary dimensions. |x|^2/2 is the only fixed point of the transform. en.wikipedia.org/wiki/Legendre_…
If you want to do high-impact research, start by reading papers from 20+ years ago. Then, apply these fundamental concepts to modern science. Most importantly, you'll get an advantage over everyone else who is only superficially parsing the latest paper feed on social media.
"I have been impressed with the urgency of doing. Knowing is not enough; we must apply. Being willing is not enough; we must do." -Leonardo da Vinci
Adding an independent Gaussian variable is the same as doing a heat diffusion on the density. en.wikipedia.org/wiki/Convoluti…
“Choosing a distribution is not a statistical question; it is a modeling decision. No statistical test can tell you whether a particular distribution is a good model for your data” - yes!
In the last week, three people on reddit/r/statistics have asked about testing whether a sample came from a Gaussian distribution. The answer is that you should never test for normality. The result is a non-answer to the wrong question. allendowney.com/blog/2023/01/2…
The farthest point sampling is a greedy algorithm which asymptotically samples from a uniform density defined by a distance (e.g. geodesic). Defines a Delone set. en.wikipedia.org/wiki/Farthest-… en.wikipedia.org/wiki/Delone_set
Most algorithmic traders only focus on the trade signal. Then they wonder why they lose money. It's not the signal that's most important. It's the filter. Here are 9 of the most popular filters everyone should know (with Python code):
3D surface compression is achieved by projecting on Fourier-like atoms defined as the eigenvectors of the combinatorial Laplacian. cs.technion.ac.il/~gotsman/Amend…
The Kubo-Ando mean generalizes geometric mean to matrices and preserves key properties (e.g. concavity and arithmetic-geometric inequality). link.springer.com/content/pdf/10…
A question on every machine learning interview: Bias, variance, and their tradeoff. Here is the explanation I wish I had when I started: 1 of 17
Least square is the most fundamental data analysis method. Gauss or Legendre? projecteuclid.org/download/pdf_1…
Fit a polynomial of degree D to N points by minimising the mean squared error. Now, start increasing D and look at the accuracy. The accuracy blows-up around D~N and starts improving *again* for D>>N 🙂 #doubleDescent #statistics #maths
Just finished filming an entire 8 week series on Differential Equations and Dynamical Systems!! This is one of my favorite topics in all of math. And this finishes up all the videos for a two-quarter, 60 hour set of lectures on Engineering Mathematics! youtube.com/watch?v=9fQkLQ…
Great technical explanation and implementation (JAX tutorial included) of diffusion models. jakiw.com/sgm_intro
Joukowsky conformal map: solving airfoil design in the pre-computer era. en.wikipedia.org/wiki/Joukowsky…
Here is a simple machine learning model. One of the classics. If you are new, let's go together line by line and understand what's happening here: 1 of 20
Excited to announce our work on novel view synthesis with diffusion models! Our model can lift a single 2d image into 3d. 3d-diffusion.github.io Joint work w/ @wchan212 @rmbrualla @hojonathanho @taiyasaki @mo_norouzi
Now in a single video: Making Friends with #MachineLearning (#MFML) Enjoy all 6.5 hours of Google’s legendary #AI course designed to enlighten AI beginners, grow technology leaders, inform better citizens, and amuse AI experts! Don't forget to RT 💖 bit.ly/funaicourse
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