The availability of representative datasets is an essential prerequisite for many successful
artificial intelligence and machine learning models. However, in real life applications these …

Neural networks are central to many emerging technologies, but verifying their correctness
remains a major challenge. It is known that network outputs can be sensitive and fragile to …

Deploying neural networks (NNs) in low-resource domains is challenging because of their
high computing, memory, and power requirements. For this reason, NNs are often quantized …

We study the problem of training and certifying adversarially robust quantized neural
networks (QNNs). Quantization is a technique for making neural networks more efficient by …

QNNVerifier is the first open-source tool for verifying implementations of neural networks that
takes into account the finite word-length (ie quantization) of their operands. The novel …

Many future technologies rely on neural networks, but verifying the correctness of their
behavior remains a major challenge. It is known that neural networks can be fragile in the …

Neural networks are an increasingly common tool for solving problems that require complex
analysis and pattern matching, such as identifying stop signs or processing medical …

The EnnCore project addresses the fundamental security problem of guaranteeing safety,
transparency, and robustness in neural-based architectures. Specifically, EnnCore aims at …