USFlows provides a stable and convenient library of flow based general purpose density models with flexibile base distributions, which are specifically tailored towards the use in neuro-symbolic verification procedures. The major goal is to provide models that can represent reference distributions which are suitable for satisfiability based approaches, abstract interpretation, and hypothesis testing simultaneously. The implemented layer are carefully designed to guarntee the following properties:
- Efficient computation of exact densities as well as efficient sampling.
- A piece-wise affine log-density function for all models with (leaky-)ReLU nonlinearity and Laplacian base distribution.
- UDL preserving layers map the upper density level sets of the data distribution to the upper density level sets of the base Distribution.
- Direct onnx export of log_prob and sampling methods.
- Clone Project:
git clone git@github.com:aai-institute/VeriFlow.git- Install poetry
curl -sSL https://install.python-poetry.org | python3 -- Finally, within the veriflow project directory:
poetry installVeriflow comes with a lightweigt experimentation library that allow effortless configuaration, e.g. of hyperparameter optimzation experiments via yaml config files. Additionally, we define several benchmarking experiments.
Within the projects script folder you'll find a a script called run_experiment.py. You can use it to conduct an experiment from the a config file.
poetry run python run_experiment.py --config <config file> --report_dir <log dir>
The Bavarian AI Act Accelerator is a two-year project funded by the Bavarian State Ministry of Digital Affairs to support SMEs, start-ups, and the public sector in Bavaria in complying with the EU AI Act. Under the leadership of the appliedAI Institute for Europe and in collaboration with Ludwig Maximilian University, the Technical University of Munich, and the Technical University of Nuremberg, training, resources, and events are being offered. The project objectives include reducing compliance costs, shortening the time to compliance, and strengthening AI innovation. To achieve these objectives, the project is divided into five work packages: project management, research, education, tools and infrastructure, and community.