Tag: computer science
All the articles with the tag "computer science".
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Optimal message passing on sparse graphs
Posted on:A condensed walkthrough of our NeurIPS 2023 paper deriving the asymptotically Bayes-optimal classifier for node classification on sparse contextual stochastic block models, and what it implies for the design of graph neural networks.
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Stein's paradox
Posted on:In three or more dimensions, the sample mean is dominated everywhere by a shrinkage estimator. The geometric reason is the Gaussian shell: noise pushes you outward, and pulling back is uniformly better. A precursor of ridge regression and most modern regularization.
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Effects of graph convolutions in multi-layer networks
Posted on:A walkthrough of our ICLR 2023 paper on how graph convolutions provably lower the feature-signal threshold for node classification in contextual stochastic block models, and why two convolutions help much more than one.
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Fast and online palindrome counting
Posted on:An exploration of an efficient algorithm for online palindrome counting using a palindrome tree data structure. Based on the work of Rubinchik and Shur, this post details the problem, the data structure, and the implementation.
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Lunch with Donald Knuth
Posted on:Reflections on a lunch meeting with Donald Knuth. Covers his thoughts on P vs NP, advice on life and curiosity, and his recent mathematical interests in families of sets.
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St. Petersburg paradox
Posted on:An analysis of the St. Petersburg paradox, where the expected winning value is infinite. Discusses the conflict between mathematical expectation and intuition, and resolves it using practical constraints.
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Common join algorithms
Posted on:An overview of common join algorithms used in database systems, including Nested Loop, Hash Join, and Sort-Merge Join. Explains the logic, implementation details, and time complexities of each.
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Implementing PEGASOS
Posted on:A detailed guide on implementing PEGASOS (Primal Estimated sub-GrAdient SOlver for SVM). Explains the mathematical derivation, the stochastic gradient descent approach, and the algorithm's steps.