/explore

Click through on any links that interest you or select the planets on the right to continue exploring the Outer Web.
You are here

 arnavdhamija.com
Introduction to Model Predictive Control ยท Arnav's Weblog
| | jxmo.io
Introduction to variational autoencoders - Jack Morris
4.3 parsecs away

Travel
| | A primer on variational autoencoders (VAEs) culminating in a PyTorch implementation of a VAE with discrete latents.
| | fa.bianp.net
On the Link Between Polynomials and Optimization, Part 1
5.4 parsecs away

Travel
| | There's a fascinating link between minimization of quadratic functions and polynomials. A link that goes deep and allows to phrase optimization problems in the language of polynomials and vice versa. Using this connection, we can tap into centuries of research in the theory of polynomials and shed new light on ...
| | blogs.princeton.edu
Guest post by Amir Ali Ahmadi: Sum of Squares (SOS) Techniques: An Introduction, Part I/II | I'm a bandit
5.3 parsecs away

Travel
| | [latexpage] Sum of squares optimization is an active area of research at the interface of algorithmic algebra and convex optimization. Over the last decade, it has made significant impact on both d...
| | francisbach.com
Scaling laws of optimization - Machine Learning Research Blog
28.4 parsecs away

Travel
| [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.