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Found 9 papers, 1 papers with code
Intrinsically motivated graph exploration using network theories of human curiosity
The theories view curiosity as an intrinsic motivation to optimize for topological features of subgraphs induced by nodes visited in the environment.
Multi-task Bias-Variance Trade-off Through Functional Constraints
In this paper we draw intuition from the two extreme learning scenarios -- a single function for all tasks, and a task-specific function that ignores the other tasks dependencies -- to propose a bias-variance trade-off.
Training Graph Neural Networks on Growing Stochastic Graphs
In this paper, we propose to learn GNNs on very large graphs by leveraging the limit object of a sequence of growing graphs, the graphon.
Learning Globally Smooth Functions on Manifolds
To do so, it shows that under typical conditions the problem of learning a Lipschitz continuous function on a manifold is equivalent to a dynamically weighted manifold regularization problem.
Federated Representation Learning via Maximal Coding Rate Reduction
We propose a federated methodology to learn low-dimensional representations from a dataset that is distributed among several clients.
Training Stable Graph Neural Networks Through Constrained Learning
Graph Neural Networks (GNN) rely on graph convolutions to learn features from network data.
An Agnostic Approach to Federated Learning with Class Imbalance
Federated Learning (FL) has emerged as the tool of choice for training deep models over heterogeneous and decentralized datasets.
Learning by Transference: Training Graph Neural Networks on Growing Graphs
Graph neural networks (GNNs) use graph convolutions to exploit network invariances and learn meaningful feature representations from network data.
Multi-task Supervised Learning via Cross-learning
In this paper we consider a problem known as multi-task learning, consisting of fitting a set of classifier or regression functions intended for solving different tasks.
