I didn't find a direct proof earlier, just assertions - I've only seen it proved in textbooks that aren't linkable.
I didn't find a direct proof earlier, just assertions - I've only seen it proved in textbooks that aren't linkable. Theorem 1, section 1.3, page 2 shows that the expected variance of the gradient of the loss function and the expected second derivative of the loss function are equal at the minimum. I hate that the ADAM paper did not talk about this, this is something that is hammered into anyone who has taken a mathematical statistics course. This has been an established fact in statistics for well over 100 years. https://courses.grainger.illinois.edu/ece563/fa2025/Notes11-... Away from the minimum they can diverge, but there is a close enough connection to make it an extremely useful approximation.
Posted by jmalicki on Jul 11, 2026, 7:56 PM
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- Because I learned on this, it made learning everything else that much harder.
- For the old school (I hate that that’s arguably applicable) AI dating types, and so on.
- > I hate that you are reducing my craft to just the end results and nothing else.
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Raise real-world problems, find people facing the same issue, and discuss solutions.2,000 issues0 comments
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Near impossible to do it robustly without AI, since a mesh is a blob of triangles and vertices, it doesn't really know if something is a ...
Because I learned on this, it made learning everything else that much harder.
For the old school (I hate that that’s arguably applicable) AI dating types, and so on.
> I hate that you are reducing my craft to just the end results and nothing else.
> This of course is the cornerstone of Western culture and no well-educated person can be without a knowledge of what is in it.
I hate that it's hidden too.
I think the jury is still out on K3/Q3.8 and if they are equivalent to Opus or Fable.
I didn't find a direct proof earlier, just assertions - I've only seen it proved in textbooks that aren't linkable.
I didn't find a direct proof earlier, just assertions - I've only seen it proved in textbooks that aren't linkable.
I didn't find a direct proof earlier, just assertions - I've only seen it proved in textbooks that aren't linkable. Theorem 1, section 1.3, page 2 shows that the expected variance of the gradient of the loss function and the expected second derivative of the loss function are equal at the minimum. I hate that the ADAM paper did not talk about this, this is something that is hammered into anyone who has taken a mathematical statistics course. This has been an established fact in statistics for well over 100 years. https://courses.grainger.illinois.edu/ece563/fa2025/Notes11-... Away from the minimum they can diverge, but there is a close enough connection to make it an extremely useful approximation.
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