Writing L1 and L2 vector norms with reverse- and forward-mode autodiff.
I study computational models of neural adaptation and gain control at NYU, supervised by Eero Simoncelli and David Heeger. I spend most of my time training and simulating neural networks, and formulating unsupervised-learning objective functions.
I'm a born-and-raised Canadian; and before coming to the US, I completed my BSc in Physiology and Physics at McGill University. I received my MSc from the University of Western Ontario, studying normalization models of attention in prefrontal cortex under the supervision of Julio Martinez-Trujillo.
My technical interests include machine learning in computer vision, numerical linear algebra, and scientific computing. Outside of research, I enjoy playing jazz guitar, cycling, and running.
pronouncing my family name, Dương: Vietnamese is a tonal language so it's hard to describe via text, but saying "Yuh-ng" will get you close. Most pronounce it like "Dew-ong"/"Dwong", which is also fine.
Training a multilayer perceptron built in pure C++.
Building a trainable multilayer perceptron in pure C++.
Creating a bare-bones linear algebra library to train a neural net.
Poisson-Identifiable Variational Autoencoder w/ PyTorch implementation.
How to reverse linked lists w/ hands tied behind your back while blindfolded underwater.
Python implementation of adaptive spiking neural net proposed in Gutierrez and Deneve eLife 2019.
Simple code to save activations of a model’s intermediate layers.