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Found 9 papers, 0 papers with code
AFAFed -- Protocol analysis
The convergence properties of AFAFed under (possibly) non-convex loss functions is guaranteed by a set of new analytical bounds, which formally unveil the impact on the resulting AFAFed convergence rate of a number of Federated Learning (FL) parameters, like, first and second moments of the per-coworker number of consecutive model updates, data skewness, communication packet-loss probability, and maximum/minimum values of the (adaptively tuned) mixing coefficient used for model aggregation.
Gomoku: analysis of the game and of the player Wine
Gomoku, also known as five in a row, is a classical board game, ideally suited for quickly testing novel Artificial Intelligence (AI) techniques.
A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling
In order to make this class of functional link adaptive filters (FLAFs) efficient, we propose low-complexity expansions and frequency-domain adaptation of the parameters.
Combined Sparse Regularization for Nonlinear Adaptive Filters
Nonlinear adaptive filters often show some sparse behavior due to the fact that not all the coefficients are equally useful for the modeling of any nonlinearity.
Why should we add early exits to neural networks?
Deep neural networks are generally designed as a stack of differentiable layers, in which a prediction is obtained only after running the full stack.
A Multimodal Deep Network for the Reconstruction of T2W MR Images
In this paper, we present a deep learning method that is able to reconstruct subsampled MR images obtained by reducing the k-space data, while maintaining a high image quality that can be used to observe brain lesions.
Efficient data augmentation using graph imputation neural networks
Recently, data augmentation in the semi-supervised regime, where unlabeled data vastly outnumbers labeled data, has received a considerable attention.
Effective Blind Source Separation Based on the Adam Algorithm
In this paper, we derive a modified InfoMax algorithm for the solution of Blind Signal Separation (BSS) problems by using advanced stochastic methods.
Learning activation functions from data using cubic spline interpolation
Neural networks require a careful design in order to perform properly on a given task.
