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destevez.net | ||
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jaberkow.wordpress.com
Lately I have been making use of a continuous relaxation of discrete random variables proposed in two recent papers: The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables and Categorical Reparameterization with Gumbel-Softmax. I decided to write a blog post with some motivation of the method, as well as providing some minor clarification on... |
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nelari.us
In inverse transform sampling, the inverse cumulative distribution function is used to generate random numbers in a given distribution. But why does this work? And how can you use it to generate random numbers in a given distribution by drawing random numbers from any arbitrary distribution? |
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reasonabledeviations.com
Academic blog about quantitative finance, programming, maths. |
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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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