6 papers
Scalar Representations of Neural Network Training Dynamics
Pedro Jiménez-González, Miguel C. Soriano, Lucas Lacasa
Training in artificial neural networks can be viewed as a trajectory evolving through a high-dimensional loss landscape. However, the large number of trainable parameters makes the…
Leveraging chaotic transients in the training of artificial neural networks
Pedro Jiménez-González, Miguel C. Soriano, Lucas Lacasa
Traditional algorithms to optimize artificial neural networks when confronted with a supervised learning task are usually exploitation-type relaxational dynamics such as gradient d…
Fluid dynamics meet network science: two cases of temporal network eigendecomposition
Lucas Lacasa
Temporal networks, defined as sequences of time-aggregated adjacency matrices, sample latent graph dynamics and trace trajectories in graph space. By interpreting each adjacency ma…
Scalar embedding of temporal network trajectories
Lucas Lacasa, F. Javier MarÃn-RodrÃguez, Naoki Masuda +1
A temporal network -- a collection of snapshots recording the evolution of a network whose links appear and disappear dynamically -- can be interpreted as a trajectory in graph spa…
Predictability of temporal network dynamics in normal ageing and brain pathology
Annalisa Caligiuri, David Papo, Görsev Yener +4
Spontaneous brain activity generically displays transient spatiotemporal coherent structures, which can selectively be affected in various neurological and psychiatric pathologies.…
Characterising the dynamics of unlabelled temporal networks
Annalisa Caligiuri, Tobias Galla, Lucas Lacasa
Networks model the architecture backbone of complex systems. The backbone itself can change over time leading to what is called `temporal networks'. Interpreting temporal networks…