1 citations · 2 across the 8 of their papers we have counts for
8 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…
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…
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…
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…
Scalar embedding of temporal network trajectories
Lucas Lacasa, F. Javier Marín-Rodríguez, 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…
Dynamical stability and chaos in artificial neural network trajectories along training
Kaloyan Danovski, Miguel C. Soriano, Lucas Lacasa
The process of training an artificial neural network involves iteratively adapting its parameters so as to minimize the error of the network's prediction, when confronted with a le…
An effective theory of collective deep learning
Lluís Arola-Fernández, Lucas Lacasa
Unraveling the emergence of collective learning in systems of coupled artificial neural networks points to broader implications for machine learning, neuroscience, and society. Her…