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 kyunghyuncho.me
Amortized Mixture of Gaussians (AMoG): A Proof of Concept for "Learning to X", or how I re-discovered simulation-based inference - Kyunghyun Cho
| | jxmo.io
Introduction to variational autoencoders - Jack Morris
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| | A primer on variational autoencoders (VAEs) culminating in a PyTorch implementation of a VAE with discrete latents.
| | iclr-blogposts.github.io
Elaborating on the Value of Flow Matching for Density Estimation | ICLR Blogposts 2024
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| | 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.
| | akosiorek.github.io
Normalizing Flows
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| | Machine learning is all about probability.To train a model, we typically tune its parameters to maximise the probability of the training dataset under the mo...
| | ischoolonline.berkeley.edu
What Is Machine Learning (ML)? - I School Online
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| Whether you know it or not, you've probably been taking advantage of the benefits of machine learning for years. Most of us would find it hard to go a full day without using at least one app or web service driven by machine learning. But what is machine learning?