Vacancies for PhD candidates, postdocs, and other positions within the ADA research group will be posted here when they become available.
PhD candidate, Robustness Verification for Meta-learned Neural Networks
Key responsibilities Neural networks achieve state-of-the-art performance on many image-recognition tasks. Despite this enormous potential, it is widely acknowledged that neural networks also need large amounts of data and high GPU requirements to achieve this performance. Also, neural networks can be subject to adversarial attacks. The data- and GPU-related limitations can be addressed by meta-learning techniques, where such a neural network is pre-trained on similar tasks. This gives rise to few-shot learning, where neural networks have shown to be competitive if as few as five examples can be provided of a given class. The possibility of an adversarial attack is often addressed by neural network verification techniques that certify the robustness of a neural network. This Ph.D. trajectory will work on the intersection of meta-learning and neural network verification.
Goal The successful candidate will carry out research on the intersection of neural network verification and meta-learning. In particular, they will identify, construct and evaluate possible benchmarks that can be used across the research community. Additionally, we will develop techniques that combine the fundamental concepts and techniques from neural network verification and meta-learning. By applying AutoML techniques (e.g., Bayesian optimization, bandit methods) on meta-learning methods, we can search for hyperparameter configurations that do not only optimize for performance, but also take into account the robustness of a neural network.