56:55 国际基础科学大会-Macroscopic Dark Matter-Yang Bai 1:01:13 国际基础科学大会-Hard and easy constraint problems-Andrei A. Bulatov 46:16 国际基础科学大会-Accessing the Nanoscale with MINFLUX Light Microscopy-Yvan Eilers 50:04 国际基础科学大会-Sampling for View Synthesis: From Local Light Field...
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The investigation of the relationship between certain binary node metadata and the network topology was performed initially by examining the correlation of the considered binary features across the network edges via the assortativity coefficient3. This coefficient, however, doesn’t take into account the ...
I avoid texts with lots of colorful but distracting pictures from authors who try hard to be funny. Also, the subjects included are somewhat focused towards my own interests. LIST OF SUBJECTS, IN LOGICAL ORDER(not everything has to be done in this order, but this approximately indicates the...
UMAP is a manifold learning method that relies on Riemannian geometry and algebraic topology to perform dimensional reduction [77]. The authors highlighted that UMAP is scalable, which turns it suitable in real applications with large datasets. Furthermore, UMAP presents results as good as t-SNE ...
But if we just look at the raw theorems from a particular axiom system—say as generated by an automated theorem-proving system—it’ll be very hard to tell what can be interpreted as being “related to numbers”. But what if we restrict ourselves to mathematical results that have been ...
“If we have a poor design/use inappropriate methods/don’t think hard about the assumptions, more data will simply make us more confident about our incorrect inference Moreover, machine learning, also a cornerstone of data science, is not a subject most Math majors would have defined until ...
thing I think we forget to do as students is to actually remember to think about what we are doing, to think about the process of mathematics, to think about the issues that we going to talk about tonight. Math is hard enough, and so we spend all the time with math which is great?
The graph is so dense that it’s hard to tell which areas have the most or least connections. Here are some tables to clarify that. First, counting how many areas an particular area contributes to, i.e. number of outgoing arrows. ...
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