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almostsuremath.com | ||
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mkatkov.wordpress.com
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| | | | | For probability space $latex (\Omega, \mathcal{F}, \mathbb{P})$ with $latex A \in \mathcal{F}$ the indicator random variable $latex {\bf 1}_A : \Omega \rightarrow \mathbb{R} = \left\{ \begin{array}{cc} 1, & \omega \in A \\ 0, & \omega \notin A \end{array} \right.$ Than expected value of the indicator variable is the probability of the event $latex \omega \in... | |
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arkadiusz-jadczyk.eu
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| | | | | In the last post, Geodesics of left invariant metrics on matrix Lie groups - Part 1,we have derived Arnold's equation - that is a half of the problem of finding geodesics on a Lie group endowed with left-invariant metric. Suppose $G$ is a Lie group, and $g(\xi,\eta)$ is a scalar product (i.e. | |
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yang-song.net
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| | | | | This blog post focuses on a promising new direction for generative modeling. We can learn score functions (gradients of log probability density functions) on a large number of noise-perturbed data distributions, then generate samples with Langevin-type sampling. The resulting generative models, often called score-based generative models, has several important advantages over existing model families: GAN-level sample quality without adversarial training, flexible model architectures, exact log-likelihood ... | |
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muijonathan.com
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| | | Preface After a long time, I have decided to resurrect the mathematics part of this blog! I started Diversions in Mathematicsas a way for me to try to explain mathematics to the general public. This continues to be the main goal of this series of blogposts -- for a more detailed introduction, please read the... | ||
