1 code implementation • 19 Oct 2021 • Jens U. Kreber, Christopher Hahn
Constructing training data for symbolic reasoning domains is challenging: Existing instances are typically hand-crafted and too few to be trained on directly and synthetically generated instances are often hard to evaluate in terms of their meaningfulness.
1 code implementation • NeurIPS 2021 • Frederik Schmitt, Christopher Hahn, Markus N. Rabe, Bernd Finkbeiner
We train hierarchical Transformers on the task of synthesizing hardware circuits directly out of high-level logical specifications in linear-time temporal logic (LTL).
no code implementations • 18 Jan 2021 • Bernd Finkbeiner, Christopher Hahn, Marvin Stenger, Leander Tentrup
Hyperproperties, such as non-interference and observational determinism, relate multiple computation traces with each other and are thus not monitorable by tools that consider computations in isolation.
Logic in Computer Science
no code implementations • 18 Jan 2021 • Bernd Finkbeiner, Christopher Hahn, Jana Hofmann, Leander Tentrup
We furthermore studied the realizability problem of HyperQPTL.
Logic in Computer Science
1 code implementation • ICLR 2021 • Christopher Hahn, Frederik Schmitt, Jens U. Kreber, Markus N. Rabe, Bernd Finkbeiner
We study two fundamental questions in neuro-symbolic computing: can deep learning tackle challenging problems in logics end-to-end, and can neural networks learn the semantics of logics.
4 code implementations • 31 May 2019 • Christopher Hahn, Marvin Stenger, Leander Tentrup
Verifying hyperproperties at runtime is a challenging problem as hyperproperties, such as non-interference and observational determinism, relate multiple computation traces with each other.
Logic in Computer Science
