31 citations · 50 across the 4 of their papers we have counts for
4 papers
Compositional generalization through abstract representations in human and artificial neural networks
Takuya Ito, Tim Klinger, Douglas H. Schultz +3
Humans have a remarkable ability to rapidly generalize to new tasks that is difficult to reproduce in artificial learning systems. Compositionality has been proposed as a key mecha…
Efficient state and parameter estimation for high-dimensional nonlinear system identification with application to MEG brain network modeling
Matthew F. Singh, Chong Wang, Michael W. Cole +1
System identification poses a significant bottleneck to characterizing and controlling complex systems. This challenge is greatest when both the system states and parameters are no…
Discovering the Computational Relevance of Brain Network Organization
Takuya Ito, Luke Hearne, Ravi Mill +2
Understanding neurocognitive computations will require not just localizing cognitive information distributed throughout the brain but also determining how that information got ther…
Similarity Learning with Higher-Order Graph Convolutions for Brain Network Analysis
Guixiang Ma, Nesreen K. Ahmed, Ted Willke +4
Learning a similarity metric has gained much attention recently, where the goal is to learn a function that maps input patterns to a target space while preserving the semantic dist…