You want to ensure that your neural network *never* crashes your control system?
Our (now accepted 🥳) #NeurIPS paper introduces:
- Reusing control theory for NN verification
- Verifying *nonlinear arithmetic* specs on NNs
This guarantees your NN won't behave like this (1/12):
If anyone in Edinburgh is interested in learning more about this work:
I will give a talk about it at the LFCS Seminar @InfAtEd on Tuesday, 29th of October, at 4 pm.
https://t.co/eDMGWnQRzG
You want to ensure that your neural network *never* crashes your control system?
Our (now accepted 🥳) #NeurIPS paper introduces:
- Reusing control theory for NN verification
- Verifying *nonlinear arithmetic* specs on NNs
This guarantees your NN won't behave like this (1/12):
@AndrePlatzer @SCSatCMU@KITinformatik Our verification tool for polynomial arithmetic specifications (possibly with prepositionally complicated structure) for (ReLU) neural networks is also open source and can be found here:
https://t.co/iv21AYPgHN
You want to ensure that your neural network *never* crashes your control system?
Our (now accepted 🥳) #NeurIPS paper introduces:
- Reusing control theory for NN verification
- Verifying *nonlinear arithmetic* specs on NNs
This guarantees your NN won't behave like this (1/12):
This project was joint work with Stefan Mitsch and @AndrePlatzer. We started working on this project during my visit to @SCSatCMU and continued working on it @KITinformatik. I'm looking forward to presenting it at #neurips2024 in Vancouver.
Preprint: https://t.co/ryA0jumu8X
@moyix Sometimes it even tells you what’s wrong:
Recently, when I ran an experiment with GPT 4o, I noticed it responded something like „I’m afraid I can’t see the prior conversation, but let me take an educated guess“ – indeed, it was not seeing most of the chat history due to a bug…
Want to learn how we can harness well-established program analysis techniques to analyse fairness properties?
Drop by my #AAAI poster (number 204) this evening!
#AAAI2024#AAAI24
Our paper "An Information-Flow Perspective on Algorithmic Fairness" was accepted for AAAI 2024:
We looked at similarities between Algorithmic Fairness and properties checked by Information Flow Analysis Tools.
Outcome: We can verify a software's fairness via Information Flow
🧵