research
∙
07/11/2023
Intrinsically motivated graph exploration using network theories of human curiosity
Intrinsically motivated exploration has proven useful for reinforcement ...
share
research
∙
10/27/2022
Multi-task Bias-Variance Trade-off Through Functional Constraints
Multi-task learning aims to acquire a set of functions, either regressor...
research
∙
10/27/2022
Training Graph Neural Networks on Growing Stochastic Graphs
Graph Neural Networks (GNNs) rely on graph convolutions to exploit meani...
research
∙
10/01/2022
Learning Globally Smooth Functions on Manifolds
Smoothness and low dimensional structures play central roles in improvin...
research
∙
10/01/2022
Federated Representation Learning via Maximal Coding Rate Reduction
We propose a federated methodology to learn low-dimensional representati...
research
∙
10/07/2021
Training Stable Graph Neural Networks Through Constrained Learning
Graph Neural Networks (GNN) rely on graph convolutions to learn features...
research
∙
06/07/2021
Increase and Conquer: Training Graph Neural Networks on Growing Graphs
Graph neural networks (GNNs) use graph convolutions to exploit network i...
research
∙
10/24/2020
