@conference {473,
	title = {A Split Kernel Adaptive Filtering Architecture for Nonlinear Acoustic Echo Cancellation},
	booktitle = {24th European Signal Processing Conference (EUSIPCO 2016)},
	year = {2016},
	month = {September},
	address = {Budapest, Hungary},
	abstract = {We propose a new linear-in-the-parameters (LIP) nonlinear filter based on kernel methods to address the problem of nonlinear acoustic echo cancellation (NAEC). For this purpose we define a framework based on a parallel scheme in which any kernel-based adaptive filter (KAF) can be incorporated efficiently. This structure is composed of a classic adaptive filter on one branch, committed to estimating the linear part of the echo path, and a kernel adaptive filter on the other branch, to model the nonlinearities rebounding in the echo path. In addition, we propose a novel low-complexity least mean square (LMS) KAF with very few parameters, to be used in the parallel architecture. Finally, we demonstrate the effectiveness of the proposed scheme in real NAEC scenarios, for different choices of the KAF.},
	isbn = {978-0-9928-6265-7},
	author = {Van Vaerenbergh, Steven and Comminiello, Danilo and Azpicueta-Ruiz, Luis A.}
}
@conference {525,
	title = {Recurrent Neural Networks With Flexible Gates Using Kernel Activation Functions},
	booktitle = {IEEE International Workshop on Machine Learning for Signal Processing},
	year = {2018},
	month = {September},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Aalborg, Denmark},
	abstract = {Gated recurrent neural networks have achieved remarkable results in the analysis of sequential data. Inside these networks, gates are used to control the flow of information, allowing to model even very long-term dependencies in the data. In this paper, we investigate whether the original gate equation (a linear projection followed by an element-wise sigmoid) can be improved. In particular, we design a more flexible architecture, with a small number of adaptable parameters, which is able to model a wider range of gating functions than the classical one. To this end, we replace the sigmoid function in the standard gate with a non-parametric formulation extending the recently proposed kernel activation function (KAF), with the addition of a residual skip-connection. A set of experiments on sequential variants of the MNIST dataset shows that the adoption of this novel gate allows to improve accuracy with a negligible cost in terms of computational power and with a large speed-up in the number of training iterations.},
	author = {Scardapane, Simone and Van Vaerenbergh, Steven and Comminiello, Danilo and Totaro, Simone and Uncini, Aurelio}
}
@conference {521,
	title = {Improving Graph Convolutional Networks with Non-Parametric Activation Functions},
	booktitle = {26th European Signal Processing Conference (EUSIPCO 2018)},
	year = {2018},
	month = {September},
	publisher = {EURASIP},
	organization = {EURASIP},
	address = {Rome, Italy},
	abstract = {Graph neural networks (GNNs) are a class of neural networks that allow to efficiently perform inference on data that is associated to a graph structure, such as, e.g., citation networks or knowledge graphs. While several variants of GNNs have been proposed, they only consider simple nonlinear activation functions in their layers, such as rectifiers or squashing functions. In this paper, we investigate the use of graph convolutional networks (GCNs) when combined with more complex activation functions, able to adapt from the training data. More specifically, we extend the recently proposed kernel activation function, a non-parametric model which can be implemented easily, can be regularized with standard l?p-norms techniques, and is smooth over its entire domain. Our experimental evaluation shows that the proposed architecture can significantly improve over its baseline, while similar improvements cannot be obtained by simply increasing the depth or size of the original GCN.},
	author = {Scardapane, Simone and Van Vaerenbergh, Steven and Comminiello, Danilo and Uncini, Aurelio}
}
