Nolan Dey

I am a MASc student in Systems Design Engineering at the University of Waterloo, where I am supervised by Bryan Tripp, Graham Taylor, and Alexander Wong. My research has focused on developing techniques to understand/explain the activation space of deep neural networks. Some of my research interests include neural network explainability, biologically-inspired deep learning, generative modelling, and understanding why deep networks generalize. I also run a Deep Learning Reading Club.

I completed my BASc in Systems Design Engineering at the University of Waterloo, where I completed machine learning internships at Mind Foundry, Apple, and Capital One and software engineering interships at Parabol, Connected, and Kik.

nsdey@uwaterloo.ca / CV / Scholar / GitHub / LinkedIn / Medium
Research
Identifying and interpreting tuning dimensions in deep networks
Shared Visual Representations in Human & Machine Intelligence NeurIPS Workshop, 2020
Nolan S. Dey, J. Eric Taylor, Bryan P. Tripp, Alexander Wong, Graham W. Taylor
[Paper] [Colab Demo]
37,000 Human-Planned Robotic Grasps With Six Degrees of Freedom
IEEE Robotics and Automation Letters, 2020
Victor R. Osorio, Rajan Iyengar, Xueyang Yao, Preshish Bhattachan, Adrian Ragobar, Nolan S. Dey, Bryan Tripp
[Paper]
Projects
Actor-Critic RL using Spiking Neurons
Nolan S. Dey

Released the only open-source implementation of “Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons” by Frémaux et al. using Nengo and OpenAI Gym

[GitHub] [Original paper]
Synthesizing Preferred Inputs for Deep Neurons via GANs
Nolan S. Dey*, Nick Torenvliet*, Austin Kothig*

Released an open-source PyTorch implementation (originally Caffe 1.X) of “Synthesizing the preferred inputs for neurons in neural networks via deep generator networks” by Nguyen et al.

[GitHub] [Original paper] [Original implementation]
Graph Convolutional Neural Network Explainability
Nolan S. Dey

Released the only open-source implementation of “Explainability Methods for Graph Convolutional Neural Networks” by Pope & Kolouri et al. using PyTorch-Geometric and RDKit

[GitHub] [Original paper]
SYDE 2019 Class Survey
Nolan S. Dey, Jason Manson-Hing

Surveyed 55 respondents from my undergraduate class with questions related to demographics, academics, internships, lifestyle, and post-graduation plans. Published a detailed analysis of the survey results with 109 graphs and open-sourced my code to help future classes conduct similar surveys

[SYDE 2019 Class Profile] [GitHub]