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Found 4 papers, 1 papers with code
Kernel-based learning with guarantees for multi-agent applications
This paper addresses a kernel-based learning problem for a network of agents locally observing a latent multidimensional, nonlinear phenomenon in a noisy environment.
A computationally lightweight safe learning algorithm
Unfortunately, Gaussian process inference scales cubically with the number of data points, limiting applicability to high-dimensional and embedded systems.
Orthogonal Transforms in Neural Networks Amount to Effective Regularization
We consider applications of neural networks in nonlinear system identification and formulate a hypothesis that adjusting general network structure by incorporating frequency information or other known orthogonal transform, should result in an efficient neural network retaining its universal properties.
Decentralized diffusion-based learning under non-parametric limited prior knowledge
We study the problem of diffusion-based network learning of a nonlinear phenomenon, $m$, from local agents' measurements collected in a noisy environment.
