Particle Life simulation in browser using WebGPU| lisyarus blog
Evolutionary algorithm| en.wikipedia.org
Stochastic gradient descent is a universal choice for optimizing deep learning models. However, it is not the only option. With black-box optimization algorithms, you can evaluate a target function $f(x): \mathbb{R}^n \to \mathbb{R}$, even when you don’t know the precise analytic form of $f(x)$ and thus cannot compute gradients or the Hessian matrix. Examples of black-box optimization methods include Simulated Annealing, Hill Climbing and Nelder-Mead method. Evolution Strategies (ES) is one...| lilianweng.github.io
I. Cholera gives you severe diarrhea, which leads to agonizing, life-threatening dehydration. Doctors long realized that cholera patients needed electrolytes, but electrolyte solutions …| SLIME MOLD TIME MOLD
Michael Edward Johnson, 11-28-19; mike@opentheory.net| Opentheory.net
I built an interactive Shiny application that uses simulated annealing to solve the famous traveling salesman problem. You can play around with it to create and solve your own tours at the bottom of …| toddwschneider.com
This blog post focuses on a promising new direction for generative modeling. We can learn score functions (gradients of log probability density functions) on a large number of noise-perturbed data distributions, then generate samples with Langevin-type sampling. The resulting generative models, often called score-based generative models, has several important advantages over existing model families: GAN-level sample quality without adversarial training, flexible model architectures, exact log...| yang-song.net
