/explore
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sander.ai | ||
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angusturner.github.io
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| | | | | Machine Learning and Data Science. | |
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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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lilianweng.github.io
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| | | | | [Updated on 2021-09-19: Highly recommend this blog post on score-based generative modeling by Yang Song (author of several key papers in the references)]. [Updated on 2022-08-27: Added classifier-free guidance, GLIDE, unCLIP and Imagen. [Updated on 2022-08-31: Added latent diffusion model. [Updated on 2024-04-13: Added progressive distillation, consistency models, and the Model Architecture section. | |
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yasoob.me
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| | | Hey guys! I recently wrote a review paper regarding the use of Machine Learning in Remote Sensing. I thought that some of you might find it interesting and insightful. It is not strictly a Python focused research paper but is interesting nonetheless. Introduction to Machine Learning and its Usage in Remote Sensing 1. Introduction Machines have allowed us to do complex computations in short amounts of time. This has given rise to an entirely different area of research which was not being explored: teachin... | ||
