generative adversarial networks

In 2014, Ian Goodfellow, then at OpenAI, published his seminal paper, titled “Generative Adversarial Networks”1, detailing how competition between generator and discriminator functions, approximated by neural networks, can train the generator to produce realistic images. In this article I will be discussing the theory behind this idea, my own implementation in Julia (mirroring the network structure in Goodfellow’s original paper), and show some of the images I was able to get out....

January 30, 2022 · 6 min

using Julia to do web based interactive plotting

So you want inline interactive plots in your blog posts? But you don’t want to learn javascript and there wasn’t an obvious way to embed matplotlib? If your comfortable with (or willing to learn) Julia, there is a simple and elegant solution! Makie.jl is a julia plotting package with a lot of functionality, including a backend system consisting of WGLMakie and JSServe which provides a way to embed makie plots—including 3d visualizations—right into html and inserted into markdown jekyll blog posts....

December 20, 2021 · 3 min

the Freeman method

Just about a year ago today, I began working on implementing an algorithm my undergrad research advisor had devised to speed up the Metropolis algorithm, in the regime where the acceptance probability is very low, which is the case in lattice simulations of quantum gravity. Quantum Gravity Physics has experienced its most rapid advancement when theories are unified: electromagnetism $\leftarrow$ electricity + magnetism + light general relativity $\leftarrow$ special relativity + curved space-time (gravity) quantum field theory $\leftarrow$ quantum mechanics + special relativity + electromagnetism + matter + nuclear forces And hopefully soon…...

June 9, 2021 · 3 min