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cgad.ski | ||
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blog.alexalemi.com
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francisbach.com
[AI summary] This text discusses the scaling laws of optimization in machine learning, focusing on asymptotic expansions for both strongly convex and non-strongly convex cases. It covers the derivation of performance bounds using techniques like Laplace's method and the behavior of random minimizers. The text also explains the 'weird' behavior observed in certain plots, where non-strongly convex bounds become tight under specific conditions. The analysis connects theoretical results to practical considerations in optimization algorithms. |
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windowsontheory.org
Previous post: ML theory with bad drawings Next post: What do neural networks learn and when do they learn it, see also all seminar posts and course webpage. Lecture video (starts in slide 2 since I hit record button 30 seconds too late - sorry!) - slides (pdf) - slides (Powerpoint with ink and animation)... |
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andrewpwheeler.com
The default hypothesis tests that software spits out when you run a regression model is the null that the coefficient equals zero. Frequently there are other more interesting tests though, and this is one I've come across often -- testing whether two coefficients are equal to one another. The big point to remember is that... |
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