2020

Depth Uncertainty in Neural Networks Talk in the Amsterdam Machine Learning Group (AMLAB)’s weekly seminar.
Slides

World Models A World Model is a generative recurrent neural network that is quickly trained in an unsupervised manner to model popular reinforcement learning environments through compressed spatio-temporal representations. Ha and Schmidhuber achieve state-of-the-art results for OpenAI Gym environments such as CarRacing-v0 by evolving a simple policy that uses these compressed representations. In our talk, we give an introduction to Markov Decision Processes and Model-based reinforcement learning (RL). Then we dissect the Ha and Schmidhuber paper and describe more recent work expanding on these ideas. Presented at Machine Learning Reading Group Talk at the Cambridge University Engineering Department, UK.
Slides

Variational Depth Search in ResNets Oral presentation at the Neural Architecture Search (NAS) workshop at ICLR 2020.
Slides

2019

Equivariance and Symmetries in CNNs

This talk discusses applications of group theory to deep learning, specifically to the design of CNNs. We focus on a few key papers from Cohen and Welling, each of which proposes new kinds of convolutional layers that enjoy equivariance to more symmetries than the standard planar-CNN we’ve all come to know and love. We motivate the use of these new convolutions, build an intuition for how they work, give some practical considerations for their use, and finally dive into the theory behind them. Presented at Machine Learning Reading Group Talk at the Cambridge University Engineering Department, UK.
Slides

Convolutional Models

Introduction to convolutional networks for image classification and spatial data. Presented at the Deep Learning Indaba 2019.
Video, Slides

A Primer on Missing Data

In the real world, datasets are often messy – it is common for values to be missing or corrupt. Examples include empty cells in spreadsheets, unanswered survey questions, or readings from faulty sensors. Unfortunately, despite the frequent occurrence of such defects, software engineers tend not to develop algorithms that are robust to missing values. As a result, many standard algorithms fail on such datasets. This talk briefly discusses the theory of missing data and practical approaches for dealing with missingness in real-world machine learning. Presented at IndabaX South Africa 2019.
Video, Slides