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Found 4 papers, 1 papers with code
RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value Factorization
Multi-agent systems are characterized by environmental uncertainty, varying policies of agents, and partial observability, which result in significant risks.
CGNN: Traffic Classification with Graph Neural Network
Extensive evaluation over real-world traffic data sets, including normal, encrypted and malicious labels, show that, CGNN improves the prediction accuracy by 23\% to 29\% for application classification, by 2\% to 37\% for malicious traffic classification, and reaches the same accuracy level for encrypted traffic classification.
S2 Reducer: High-Performance Sparse Communication to Accelerate Distributed Deep Learning
Distributed stochastic gradient descent (SGD) approach has been widely used in large-scale deep learning, and the gradient collective method is vital to ensure the training scalability of the distributed deep learning system.
Nyström Approximation with Nonnegative Matrix Factorization
Motivated by the needs of estimating the proximity clustering with partial distance measurements from vantage points or landmarks for remote networked systems, we show that the proximity clustering problem can be effectively formulated as the Nystr\"om approximation problem, which solves the kernel K-means clustering problem in the complex space.
