Maximilian Du

Media Annotator

In my interviews and media review, I often find myself needing a real-time annotator. So I coded one! It listens for your keystrokes anywhere on the screen, so you can be focused on your media. It uses system time, so it runs in lockstep with any media player. It exports your annotations to a simple text format that you can add to any literature review notes. And best of all, your annotations are attached to simple macros (number keys) and can be easily changed!

Code

MidiStyle: Audio Style Transfer

CS 229 Final Project

Style transfer is pretty pervasive in visual tasks, using anything from Gram Matrix methods to CycleGAN. Can we try using established vision style transfer algorithms on audio? In this project, we show that this is indeed possible. Using a spectrogram representation, we change a piano into harp, harpsichord, electric guitar, and even timpanis. We generate our own data using MIDI, and we test on a real-world piano.

Website   /   Code

Coding Basics

Literally every time I want to make a line plot in Matplotlib, or save a model in PyTorch, or load a csv, I find myself searching it up and copy/pasting. To unify all of these simple things, I'm working on a large repository of code basics for researchers. This includes Numpy, PyTorch, LaTeX, Pandas, fileloading, Matplotlib, Seaborn, and others. I'm also including easily runnable PyTorch implementations of common ML models.

Code

Thompson Sampling Simulator

CS 109 Winning Final Project

The Multi-Armed Bandit is a fascinating theoretical question, but it is also a compelling question of philosophical intensity: how do we balance exploration vs exploitation? We look at one algorithm, known as Thompson sampling. Here, we simulate ants finding a good location for a nest. We also implement Tandem Running, which allows ants to "persuade" other ants, resulting in faster convergence.

Simulation   /   Code

The Tricks, Goofs, and Whales of Machine Learning

Machine learning is the abstraction of real learning processes into computational ones. Therefore, we find that it often imitates nature. In fact, certain truths in machine learning can be used to formalize our understandings of the world. In this 90 minute talk, I explore this virtuous cycle in from many different perspectives. I look at animal training as a reflection of reinforcement learning, games and arms races as a reflection of adversarial paradigms, learned helplessness & religion in the context of information theory, and others. This talk was originally made for Stanford Splash, but it works for any audience of high schoolers. It can also be adapted for older audiences.

Whales & Society: A Database

Not to be all pretentious or anything, but I think I'm building up what will become the world's most comprehensive archival database on the subject of whales in captivity. Right now, it contains more than 3.5k individual documents, including images, videos, books, audio, web snapshots, reports, and research articles, all sorted and indexed. After I finish my book, I can release most of it for educational use.