207 citations · 1.2k across the 102 of their papers we have counts for
7 papers · 2 filters
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…
Intrinsically motivated graph exploration using network theories of human curiosity
Shubhankar P. Patankar, Mathieu Ouellet, Juan Cervino +3
Intrinsically motivated exploration has proven useful for reinforcement learning, even without additional extrinsic rewards. When the environment is naturally represented as a grap…
Geometric Graph Filters and Neural Networks: Limit Properties and Discriminability Trade-offs
Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro
This paper studies the relationship between a graph neural network (GNN) and a manifold neural network (MNN) when the graph is constructed from a set of points sampled from the man…
Solving Large-scale Spatial Problems with Convolutional Neural Networks
Damian Owerko, Charilaos I. Kanatsoulis, Alejandro Ribeiro
Over the past decade, deep learning research has been accelerated by increasingly powerful hardware, which facilitated rapid growth in the model complexity and the amount of data i…
Resilient Constrained Learning
Ignacio Hounie, Alejandro Ribeiro, Luiz F. O. Chamon
When deploying machine learning solutions, they must satisfy multiple requirements beyond accuracy, such as fairness, robustness, or safety. These requirements are imposed during t…
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…