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.
An example of a custom TensorFlow operation implemented in C++.
Tutorials for (deep convolutional) neural networks.
Torch/CUDA implementation of batch normalization for OctNets.
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.
Examples, tools and resources for using Caffe’s Python interface pyCaffe.
A template for extending PyTorch using C/CUDA operations.
Basic and advanced torch examples, template for implementing custom C/CUDA modules and implementations of variational auto-encoders.
3D mesh fusion, voxelization and evaluation for computer vision research.