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francisbach.com | ||
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jeremykun.com
Hard to believe Sanjeev Arora and his coauthors consider it"a basic tool [that should be] taught to all algorithms students together with divide-and-conquer, dynamic programming, and random sampling."Christos Papadimitriou calls it"so hard to believe that it has been discovered five times and forgotten." It has formed the basis of algorithms inmachine learning, optimization, game theory, |
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aria42.com
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. |
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www.jeremykun.com
This post is a sequel to Formulating the Support Vector Machine Optimization Problem. The Karush-Kuhn-Tucker theorem Generic optimization problems are hard to solve efficiently. However, optimization problems whose objective and constraints have special structure often succumb to analytic simplifications. For example, if you want to optimize a linear function subject to linear equality constraints, one can compute the Lagrangian of the system and find the zeros of its gradient. More generally, optimizing... |
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blog.evjang.com
This tutorial will show you how to use normalizing flows like MAF, IAF, and Real-NVP to deform an isotropic 2D Gaussian into a complex cl... |
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