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vincebuffalo.com | ||
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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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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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gcbias.org
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| | | | | In my last couple of posts I talked about how much of your (autosomal) genome you inherit from a particular ancestor [1,2]. In the chart below I show a family tree radiating out from one individual. Each successive layer out shows an individual's ancestors another generation back in time, parents, grandparents, great-grandparents and so on... | |
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www.shaped.ai
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| | | In this article, we'll take a deep dive into PinSage, a state-of-the-art GCN framework developed at Pinterest for learning high-quality embeddings of nodes in massive, billion-scale graphs. Through a novel combination of techniques spanning sampling, dynamic graph construction and distributed computing, PinSage achieves order-of-magnitude speedups over existing GCN approaches while delivering substantial gains in recommendation performance. Understanding the innovations powering PinSage provides a window into the frontier of deploying deep learning on web-scale systems. | ||
