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On the Convergence of the Unadjusted Langevin Algorithm
| | francisbach.com
Scaling laws of optimization - Machine Learning Research Blog
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| | [AI summary] This text discusses the scaling laws of optimization in machine learning, focusing on asymptotic expansions for both strongly convex and non-strongly convex cases. It covers the derivation of performance bounds using techniques like Laplace's method and the behavior of random minimizers. The text also explains the 'weird' behavior observed in certain plots, where non-strongly convex bounds become tight under specific conditions. The analysis connects theoretical results to practical considerations in optimization algorithms.
| | aria42.com
Numerical Optimization: Understanding L-BFGS - aria42
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| | Numerical optimization is at the core of much of machine learning. In this post, we derive the L-BFGS algorithm, commonly used in batch machine learning applications.
| | dustintran.com
On asymptotic convergence of averaged SGD | Dustin Tran
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| | Stochastic gradient descent (SGD) has seen wide application for learning problems on large scale data, whether this be for generalized linear models [6], SVM...
| | www.nicktasios.nl
Latent Diffusion Series: MNIST Classifier | NICK TASIOS
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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