IAM

DAVIDSTUTZ

TAG»MACHINE LEARNING«

ARTICLE

PhD Thesis on Robustness and Uncertainty in Deep Learning

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.

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13thNOVEMBER2022

PROJECT

An example of a custom TensorFlow operation implemented in C++.

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08thNOVEMBER2022

PROJECT

Tutorials for (deep convolutional) neural networks.

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07thNOVEMBER2022

PROJECT

A C++ implementation of density forests.

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06thNOVEMBER2022

PROJECT

PhD thesis on uncertainty estimation and (adversarial) robustness in deep learning.

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ARTICLE

PhD Defense Slides and Lessons Learned

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.

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ARTICLE

How I Prepared for DeepMind and Google AI Research Internship Interviews in 2019

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.

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18thAUGUST2022

PROJECT

Examples, tools and resources for using Caffe’s Python interface pyCaffe.

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17thAUGUST2022

PROJECT

A template for extending PyTorch using C/CUDA operations.

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ARTICLE

Code Released: Conformal Training

The code for our ICLR’22 paper on learning optimal conformal classifiers is now available on GitHub. The repository not only includes our implementation of conformal training but also relevant baselines such as coverage training and several conformal predictors for evaluation. Furthermore, it allows to reproduce the majority of experiments from the paper.

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