Improving Expressivity of Graph Neural Networks. - Stanislaw J. Purgal (IJCNN, 2020)

Deep Learning for Automated Theorem Proving - Extended Abstract. - Stanislaw J. Purgal (CICM Workshops, 2021)

A Study of Continuous Vector Representationsfor Theorem Proving. - Stanislaw J. Purgal; Julian Parsert; Cezary Kaliszyk (CoRR, 2021)

Learning Higher-Order Programs without Meta-Interpretive Learning. - Stanislaw J. Purgal; David M. Cerna; Cezary Kaliszyk (CoRR, 2021)

A study of continuous vector representations for theorem proving. - Stanislaw J. Purgal; Julian Parsert; Cezary Kaliszyk (J. Log. Comput., 2021)

Adversarial Learning to Reason in an Arbitrary Logic. - Stanislaw J. Purgal; Cezary Kaliszyk (FLAIRS, 2022)

Learning Higher-Order Logic Programs From Failures. - Stanislaw J. Purgal; David M. Cerna; Cezary Kaliszyk (IJCAI, 2022)

Differentiable Inductive Logic Programming in High-Dimensional Space. - Stanislaw J. Purgal; David M. Cerna; Cezary Kaliszyk (CoRR, 2022)