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distill.pub | ||
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christopher-beckham.github.io
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| | | | | Vicinal distributions as a statistical view on data augmentation | |
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fanpu.io
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| | | | | Deep learning is currently dominated by parametric models, which are models with a fixed number of parameters regardless of the size of the training dataset. Examples include linear regression models and neural networks. However, it's good to occasionally take a step back and remember that that is not all there is. Non-parametric models like k-NN, decision trees, or kernel density estimation don't rely on a fixed set of weights, but instead grow in complexity based on the size of the data. In this post we'll talk about Gaussian processes, a conceptually important, but in my opinion under-appreciated non-parametric approach with deep connections with modern-day neural networks. An intersting motivating fact which we will eventually show is that neural networks initialized with Gaussian weights are equivalent to Gaussian processes in the infinite-width limit. | |
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www.huber.embl.de
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| | | | | If you are a biologist and want to get the best out of the powerful methods of modern computational statistics, this is your book. | |
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www.nicktasios.nl
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| | | In the Latent Diffusion Series of blog posts, I'm going through all components needed to train a latent diffusion model to generate random digits from the MNIST dataset. In this first post, we will tr | ||
