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I have an undergraduate degree in Math and generally find math quite enjoyable, however, even I had a pretty hard time grasping most of what this blog was talki
by 3vidence 2y ago
I have an undergraduate degree in Math and generally find math quite enjoyable, however, even I had a pretty hard time grasping most of what this blog was talking about.
I think it pretty quickly went from extremely high level (add noise to image, then remove noise) to extremely low level specific.
Also I had a hard time figuring out which part of the equation differentiate the equations for different data points, is that what the meaning of "theta" is in all the equations?
To guide the initial noise towards one type of image instead of another is that what theta is responsible for? Is the innovation in GenAI images to use text embeddings to create the theta?
I definitley feel some tears coming on.
- Sharlin 2y agoYeah, the article could definitely use some clarification in many parts (the author may be suffering from the curse of knowledge a bit). Plus there's the fact that even if you know your ODEs, SDEs are a different beast bringing in probability (certainly one may not be accustomed to seeing `p(x|y)` in the middle of a differential equation…)
- sl8r 2y agoHey, I’m the post author — thanks for reading. Theta represents all the model params — all the weights in the neural network. The convention is to write theta for the “learned” score function and omit theta for the “true” score function.