Deep neural networks have exhibited substantial success in the field of Natural Language
Processing and ensuring their safety and reliability is crucial: there are safety critical …

Extensive research on formal verification of machine learning systems indicates that
learning from data alone often fails to capture underlying background knowledge such as …

Abstract Machine learning components, such as neural networks, gradually make their way
into software; and, when the software is critically safe, the machine learning components …

Neuro-symbolic programs--programs containing both machine learning components and
traditional symbolic code--are becoming increasingly widespread. However, we believe that …

Verification of machine learning models used in Natural Language Processing (NLP) is
known to be a hard problem. In particular, many known neural network verification methods …

Extensive research on formal verification of machine learning (ML) systems indicates that
learning from data alone often fails to capture underlying background knowledge. A variety …

The field of neuro-symbolic artificial intelligence (NeSy), which combines learning and
reasoning, has recently experienced significant growth. There now are a wide variety of …

Recent direct preference alignment algorithms (DPA), such as DPO, have shown great
promise in aligning large language models to human preferences. While this has motivated …

The increased reliance of self-driving vehicles on neural networks opens up the challenge of
their verification. In this paper we present an experience report, describing a case study …

We explore a kind of first-order predicate logic with intended semantics in the reals.
Compared to other approaches in the literature, we work predominantly in the multiplicative …