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Found 4 papers, 2 papers with code
Transferability of Convolutional Neural Networks in Stationary Learning Tasks
To accomplish this we investigate the properties of CNNs for tasks where the underlying signals are stationary.
Solving Large-scale Spatial Problems with Convolutional Neural Networks
Over the past decade, deep learning research has been accelerated by increasingly powerful hardware, which facilitated rapid growth in the model complexity and the amount of data ingested.
Multi-Target Tracking with Transferable Convolutional Neural Networks
Multi-target tracking (MTT) is a classical signal processing task, where the goal is to estimate the states of an unknown number of moving targets from noisy sensor measurements.
Unsupervised Optimal Power Flow Using Graph Neural Networks
Optimal power flow (OPF) is a critical optimization problem that allocates power to the generators in order to satisfy the demand at a minimum cost.
