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You are here		seanzhang.me EM in a nutshell - Ramblings on Deep Learning
\|	\|	fa.bianp.net On the Convergence of the Unadjusted Langevin Algorithm The Langevin algorithm is a simple and powerful method to sample from a probability distribution. It's a key ingredient of some machine learning methods such as diffusion models and differentially private learning. In this post, I'll derive a simple convergence analysis of this method in the special case when the ...	3.3 parsecs Travel
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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	3.7 parsecs Travel
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\|	\|	francisbach.com Scaling laws of optimization - Machine Learning Research Blog [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.	3.6 parsecs Travel
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\|	\|	values.utdallas.edu Videos - 2024 Paul Feyerabend Conference - VMST-12 - Center for Values in Medicine, Science, and Technology \| The University of Texas at Dallas Links to videos from the 2024 VMST-12 conference ("Paul K. Feyerabend: A Centennial Celebration") are posted below. This four-day conference w...	16.3 parsecs Travel
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