Showing cs.LGShow all
2 papers · 1 filter
cs.LG2023
Learning Linear Embeddings for Non-Linear Network Dynamics with Koopman Message Passing
King Fai Yeh, Paris Flood, William Redman +1
Recently, Koopman operator theory has become a powerful tool for developing linear representations of non-linear dynamical systems. However, existing data-driven applications of Ko…
cs.LG2020
Investigating Estimated Kolmogorov Complexity as a Means of Regularization for Link Prediction
Paris D. L. Flood, Ramon Viñas, Pietro Liò
Link prediction in graphs is an important task in the fields of network science and machine learning. We investigate a flexible means of regularization for link prediction based on…