Adaptive recurrent spiking autoencoder
Python implementation of adaptive spiking neural net proposed in Gutierrez and Deneve eLife 2019.
about me
I'm a PhD student at NYU studying computational models of neural population adaptation, supervised by Eero Simoncelli and David Heeger. My dissertation focuses on adaptive gain control in recurrent neural networks, and developing statistical methods for analyzing artificial and biological neural repesentations.
In the Summer of 2022, I was a PhD Intern on the Open Codecs team at Google, Mountain View, CA. My research project was on adaptive auto-encoders and efficient nonlinear transforms for next-generation video compression.
I'm a born-and-raised Canadian 🍁. I received my BSc in Physiology and Physics at McGill University, where I began working on what would eventually become my MSc obtained from the University of Western Ontario (our lab migrated), modeling neural correlates of visuo-spatial attention in prefrontal cortex under the supervision of Julio Martinez-Trujillo.
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.
recent posts
Saving PyTorch activations w/ forward hooks
Simple code to save activations of a model’s intermediate layers.
PyTorch Randomized adaptive range finder
A cool algorithm that adaptively approximates the column space of a matrix using random projections.
Normalizing Flows and RealNVP
Simple implenetation of the RealNVP invertible neural network.
Batch and instance whitening
This short post will cover graphical intuition and PyTorch code for two different kinds of whitening: batch and instance.
Orthogonal iteration
A tutorial for a classic numerical linear algebra algorithm to find eigenvectors and values.
Elliptical slice sampling
A Python implementation of the elegant algorithm introduced by Iain Murray et al. (2010).
Interpolating covariances with their geometric mean
What’s the best way to quantify and visualize distance between two positive definite matrices? Julia code included.