Machine learning has evolved into an enabling technology for a wide range of highly
successful applications. The potential for this success to continue and accelerate has placed …

Data augmentation has recently seen increased interest in NLP due to more work in low-
resource domains, new tasks, and the popularity of large-scale neural networks that require …

The open-world deployment of Machine Learning (ML) algorithms in safety-critical
applications such as autonomous vehicles needs to address a variety of ML vulnerabilities …

We propose to harness the potential of simulation for semantic segmentation of real-world
self-driving scenes in a domain generalization fashion. The segmentation network is trained …

One of the main challenges in drug discovery is predicting protein–ligand binding affinity.
Recently, machine learning approaches have made substantial progress on this task …

We present VerifAI, a software toolkit for the formal design and analysis of systems that
include artificial intelligence (AI) and machine learning (ML) components. VerifAI particularly …

We present a new approach to automated scenario-based testing of the safety of
autonomous vehicles, especially those using advanced artificial intelligence-based …

Autonomous vehicles are complex systems that are challenging to test and debug. A
requirements-driven approach to the development process can decrease the resources …

Semantic segmentation is a crucial image understanding task, where each pixel of image is
categorized into a corresponding label. Since the pixel-wise labeling for ground-truth is …

We propose a new probabilistic programming language for the design and analysis of cyber-
physical systems, especially those based on machine learning. We consider several …