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Found 6 papers, 0 papers with code
Hybrid FedGraph: An efficient hybrid federated learning algorithm using graph convolutional neural network
Federated learning is an emerging paradigm for decentralized training of machine learning models on distributed clients, without revealing the data to the central server.
Learning Multiple Coordinated Agents under Directed Acyclic Graph Constraints
This paper proposes a novel multi-agent reinforcement learning (MARL) method to learn multiple coordinated agents under directed acyclic graph (DAG) constraints.
Synthetic Unknown Class Learning for Learning Unknowns
By learning the unknown-like samples and known samples in an alternating manner, the proposed method can not only experience diverse synthetic unknowns but also reduce overgeneralization with respect to known classes.
Teacher-Explorer-Student Learning: A Novel Learning Method for Open Set Recognition
To address this problem, teacher-explorer-student (T/E/S) learning, which adopts the concept of open set recognition (OSR) that aims to reject unknown samples while minimizing the loss of classification performance on known samples, is proposed in this study.
Collective Decision of One-vs-Rest Networks for Open Set Recognition
For this purpose, a novel network structure is proposed, in which multiple one-vs-rest networks (OVRNs) follow a convolutional neural network feature extractor.
One-vs-Rest Network-based Deep Probability Model for Open Set Recognition
Furthermore, the network yields a sophisticated nonlinear features-to-output mapping that is explainable in the feature space.
