In March this year I finally submitted my PhD thesis and successfully defended in July. Now, more than 6 months later, my thesis is finally available in the university’s library. During my PhD, I worked on various topics surrounding robustness and uncertainty in deep learning, including adversarial robustness, robustness to bit errors, out-of-distribution detection and conformal prediction. In this article, I want to share my thesis and give an overview of its contents.
Several mathematical impage processing exercises implemented in C++ and MatLab.
Tutorials for (deep convolutional) neural networks.
Torch/CUDA implementation of batch normalization for OctNets.
The Berkeley Segmentation Benchmark extended by superpixel metrics.
OPEN SOURCE blenderpy Mesh/Voxel Visualization Figure 1 (click to enlarge): Visualization examples of an occupancy grid (left) and a mesh (right) of a chair. The right visualization also shows a point cloud observation (in red). Blender is an open-source “3D creation suite” — a tool for creating and manipulating 3D shapes and scenes. While I […]
Tools to pre-process the NYU Depth v2 segmentations for evaluation.
PhD thesis on uncertainty estimation and (adversarial) robustness in deep learning.
In July this year I finally defended my PhD which mainly focused on (adversarial) robustness and uncertainty estimation in deep learning. In my case, the defense consisted of a (public) 30 minute talk about my work, followed by questions from the thesis committee and audience. In this article, I want to share the slides and some lessons learned in preparing for my defense.
In 2019, I interviewed for research internships at DeepMind and Google AI. I have been asked repeatedly about my preparation for and experience with these interviews. As internship applications at DeepMind have been opened recently, I thought it could be valuable to summarize my experience and recommendations in this article.