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akosiorek.github.io | ||
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iclr-blogposts.github.io
The transfer of matching-based training from Diffusion Models to Normalizing Flows allows to fit expressive continuous normalizing flows efficiently and therefore enables their usage for different kinds of density estimation tasks. One particularly interesting task is Simulation-Based Inference, where Flow Matching enabled several improvements. The post shall focus on the discussion of Flow Matching for Continuous Normalizing Flows. To highlight the relevance and the practicality of the method, their use and advantages for Simulation-Based Inference is elaborated. |
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jaketae.github.io
Note: This blog post was completed as part of Yale's CPSC 482: Current Topics in Applied Machine Learning. |
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lilianweng.github.io
So far, I've written about two types of generative models, GAN and VAE. Neither of them explicitly learns the probability density function of real data, $p(\mathbf{x})$ (where $\mathbf{x} \in \mathcal{D}$) - because it is really hard! Taking the generative model with latent variables as an example, $p(\mathbf{x}) = \int p(\mathbf{x}\vert\mathbf{z})p(\mathbf{z})d\mathbf{z}$ can hardly be calculated as it is intractable to go through all possible values of the latent code $\mathbf{z}$. |
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liorsinai.github.io
A denoising diffusion probabilistic model for generating numbers based on the MNIST dataset. The underlying machine learning model is a U-Net model, which is... |
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