131 citations · 200 across the 27 of their papers we have counts for
10 papers · 1 filter
Distributed Training of Large Graph Neural Networks with Variable Communication Rates
Juan Cervino, Md Asadullah Turja, Hesham Mostafa +2
Training Graph Neural Networks (GNNs) on large graphs presents unique challenges due to the large memory and computing requirements. Distributed GNN training, where the graph is pa…
Near-Optimal Solutions of Constrained Learning Problems
Juan Elenter, Luiz F. O. Chamon, Alejandro Ribeiro
With the widespread adoption of machine learning systems, the need to curtail their behavior has become increasingly apparent. This is evidenced by recent advancements towards deve…
Neural Tangent Kernels Motivate Graph Neural Networks with Cross-Covariance Graphs
Shervin Khalafi, Saurabh Sihag, Alejandro Ribeiro
Neural tangent kernels (NTKs) provide a theoretical regime to analyze the learning and generalization behavior of over-parametrized neural networks. For a supervised learning task,…
Non Commutative Convolutional Signal Models in Neural Networks: Stability to Small Deformations
Alejandro Parada-Mayorga, Landon Butler, Alejandro Ribeiro
In this paper we discuss the results recently published in~[1] about algebraic signal models (ASMs) based on non commutative algebras and their use in convolutional neural networks…
Transferability of Convolutional Neural Networks in Stationary Learning Tasks
Damian Owerko, Charilaos I. Kanatsoulis, Jennifer Bondarchuk +2
Recent advances in hardware and big data acquisition have accelerated the development of deep learning techniques. For an extended period of time, increasing the model complexity h…
Transferability of coVariance Neural Networks and Application to Interpretable Brain Age Prediction using Anatomical Features
Saurabh Sihag, Gonzalo Mateos, Corey T. McMillan +1
Graph convolutional networks (GCN) leverage topology-driven graph convolutional operations to combine information across the graph for inference tasks. In our recent work, we have…