3 papers
cs.LG2020
On Correctness of Automatic Differentiation for Non-Differentiable Functions
Wonyeol Lee, Hangyeol Yu, Xavier Rival +1
Differentiation lies at the core of many machine-learning algorithms, and is well-supported by popular autodiff systems, such as TensorFlow and PyTorch. Originally, these systems h…
cs.CR2020
Automatically Proving Microkernels Free from Privilege Escalation from their Executable
Olivier Nicole, Matthieu Lemerre, Sébastien Bardin +1
Operating system kernels are the security keystone of most computer systems, as they provide the core protection mechanisms. Kernels are in particular responsible for their own sec…
cs.PL2019
Towards Verified Stochastic Variational Inference for Probabilistic Programs
Wonyeol Lee, Hangyeol Yu, Xavier Rival +1
Probabilistic programming is the idea of writing models from statistics and machine learning using program notations and reasoning about these models using generic inference engine…