Differentiable Logics for Neural Network Training and Verification. - Natalia Slusarz; Ekaterina Komendantskaya; Matthew L. Daggitt; Robert J. Stewart 001 (NSV/FoMLAS@CAV, 2022)

The Vehicle Tutorial: Neural Network Verification with Vehicle. - Matthew L. Daggitt; Wen Kokke; Ekaterina Komendantskaya; Robert Atkey; Luca Arnaboldi 001; Natalia Slusarz; Marco Casadio; Ben Coke; Jeonghyeon Lee (FoMLAS@CAV, 2023)

Logic of Differentiable Logics: Towards a Uniform Semantics of DL. - Natalia Slusarz; Ekaterina Komendantskaya; Matthew L. Daggitt; Robert J. Stewart 001; Kathrin Stark (LPAR, 2023)

Taming Differentiable Logics with Coq Formalisation. - Reynald Affeldt; Alessandro Bruni; Ekaterina Komendantskaya; Natalia Slusarz; Kathrin Stark (ITP, 2024)

Vehicle: Bridging the Embedding Gap in the Verification of Neuro-Symbolic Programs. - Matthew L. Daggitt; Wen Kokke; Robert Atkey; Natalia Slusarz; Luca Arnaboldi 001; Ekaterina Komendantskaya (CoRR, 2024)

A Neurosymbolic Framework for Bias Correction in CNNs. - Parth Padalkar; Natalia Slusarz; Ekaterina Komendantskaya; Gopal Gupta 001 (CoRR, 2024)

A Neurosymbolic Framework for Bias Correction in Convolutional Neural Networks. - Parth Padalkar; Natalia Slusarz; Ekaterina Komendantskaya; Gopal Gupta 001 (Theory Pract. Log. Program., 2024)

Formalisation of Differentiable Logics in Coq. - Natalia Slusarz; Reynald Affeldt; Alessandro Bruni (DROPS Artifacts, 2024)

Vehicle: Bridging the Embedding Gap in the Verification of Neuro-Symbolic Programs (Invited Talk). - Matthew L. Daggitt; Wen Kokke; Robert Atkey; Ekaterina Komendantskaya; Natalia Slusarz; Luca Arnaboldi 001 (FSCD, 2025)