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lh3.github.io | ||
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algorithmsoup.wordpress.com
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| | | | | In this post, I want to tell you about what I think might be the world's simplest interesting algorithm. The vertex cover problem. Given a graph $latex {G = (V, E)}&fg=000000$, we want to find the smallest set of vertices $latex {S \subseteq V}&fg=000000$ such that every edge $latex {e \in E}&fg=000000$ is covered by... | |
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dominiczypen.wordpress.com
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| | | | | Suppose you want to have a graph $latex G = (V,E)$ with chromatic number $latex \chi(G)$ equaling some value $latex k$, such that $latex G$ is minimal with this property. So you end up with a $latex k$-(vertex-)critical graph. It is easy to construct critical graphs by starting with some easy-to-verify example like $latex C_5$... | |
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swethatanamala.github.io
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| | | | | As a series of posts, I would be working and explaining on deep graph neural networks. So, In this blog I give introduction to Graph theory | |
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www.paepper.com
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| | | When you have a big data set and a complicated machine learning problem, chances are that training your model takes a couple of days even on a modern GPU. However, it is well-known that the cycle of having a new idea, implementing it and then verifying it should be as quick as possible. This is to ensure that you can efficiently test out new ideas. If you need to wait for a whole week for your training run, this becomes very inefficient. | ||
