Publications
Synthetic Cancer - Augmenting Worms with LLMs
Submission to the Swiss AI Safety Prize
Joint work with Benjamin Zimmerman.
🥇🎉 We are thrilled to announce that we have won the first prize and received 7500 CHF!
Our submission presents a novel malware approach that enhances worms with Large Language Models (LLMs). The proposed metamorphic malware utilizes LLMs for code rewriting and targeted spreading through social engineering.
Towards a Deeper Understanding of Semantic Comprehension in Language Models Paired with Semantic Graphs
Joint work with Matthias Kleiner and Ahmet Özüdogru.
We extend previous work by (Wu et al., 2021) who proposed infusing additional semantic information in the form of DM graphs into graph neural networks stacked on top of language models into models for language tasks. We present a detailed ablation study on semantic understanding in these models and extend them using AMR graphs.
Applying Predictability Minimization onto Unsupervised Contrastive Representation Learning
We demonstrate that existing methods for unsupervised contrastive representation learningsignificantly reduces linear redundancy in the embedding space. With the goal of first quantifying and then reducing the amount of higher- order dependencies, we devise a novel neural network architecture incorporating predictability minimization (PM) into unsupervised representation learning by having two neural networks play a competitive game.
- Links coming soon.
ETH Materials
This is a collection of some materials I have written while studying at ETH Zürich. Since they might be useful for others too, I have decided to make them available on the internet.
More links will follow in the future.
More Material
If you are looking for more I can wholeheartedly recommend Sven Pfiffner's website.
Analysis 1 & 2
For the ETH Ananlysis lectures I have written two summaries which I have used in the exams:
Licensed under the permissive MIT License. Your help is greatly appreciated! Feel free to send me an email or to open an issue when you find mistakes.
Linear Algebra
A handwritten Linear Algebra summary written for the final exam. The summary was written in fall of 2018.
Probability and Statistics
For spring's Probability and Statistics lecture given by Prof. Teichmann I have written a short summary with key methods and theorems.
The summary is in German as the class was taught in German!
Licensed under the permissive Attribution-ShareAlike 4.0 International. Your help is greatly appreciated! Feel free to send me an email or to open an issue when you find mistakes.
Numerical Methods for CSE
A summary which I have taken to the exam. It mainly consists of Eigen documentation and concepts that could not easily be found on the provided lecture notes.
The summary might have some rough edges.
Licensed under the permissive Creative Commons Attribution-NonCommercial 4.0 International License. Your help is greatly appreciated; Feel free to send me an email or to open an issue when you find mistakes.
Promela Cheatsheet
A small summary with the main language constructs of the promela language used by the spin model checker. Written in spring of 2020 for the class "Functional Programming and Formal Methods" at ETH Zürich.
Licensed under the permissive MIT License. Your help is greatly appreciated; Feel free to send me an email or to open an issue when you find mistakes.
SQL Cheatsheet
A quite comprehensive SQL cheatsheet covering all SQL constructs covered in class. Written in Spring of 2020.
Licensed under the permissive MIT License. Your help is greatly appreciated; Feel free to send me an email or to open an issue when you find mistakes.
Algorithms Collection for Freshmen Algorithms Classes at ETH
During the first year of my CS studies I have kept a repository where I have implemented a majority of the algorithms shown in class.
The repository definitely has some rough edges as many algorithms were implemented under time constraints, but all in all it should contain a wide variety of algorithms.
ETH Introduction to Machine Learning Cheatsheet 2021
Exam summary for the course Introduction to Machine Learning (2021) at ETH Zürich. The summary is an adapted and slightly updated version of the outstanding summary eth-cs-student-summaries / Introduction-to-Machine-Learning summary. Please check out the original version!