
A Classification of Artificial Intelligence Systems for Mathematics Education
This chapter provides an overview of the different Artificial Intelligen...
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On the Stability and Generalization of Learning with Kernel Activation Functions
In this brief we investigate the generalization properties of a recently...
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Widely Linear Kernels for ComplexValued Kernel Activation Functions
Complexvalued neural networks (CVNNs) have been shown to be powerful no...
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Recurrent Neural Networks with Flexible Gates using Kernel Activation Functions
Gated recurrent neural networks have achieved remarkable results in the ...
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Improving Graph Convolutional Networks with NonParametric Activation Functions
Graph neural networks (GNNs) are a class of neural networks that allow t...
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Complexvalued Neural Networks with Nonparametric Activation Functions
Complexvalued neural networks (CVNNs) are a powerful modeling tool for ...
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Pattern Localization in Time Series through SignalToModel Alignment in Latent Space
In this paper, we study the problem of locating a predefined sequence of...
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Kafnets: kernelbased nonparametric activation functions for neural networks
Neural networks are generally built by interleaving (adaptable) linear l...
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Recursive Multikernel Filters Exploiting Nonlinear Temporal Structure
In kernel methods, temporal information on the data is commonly included...
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On the Relationship between Online Gaussian Process Regression and Kernel Least Mean Squares Algorithms
We study the relationship between online Gaussian process (GP) regressio...
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A Probabilistic LeastMeanSquares Filter
We introduce a probabilistic approach to the LMS filter. By means of an ...
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Gaussian Processes for Nonlinear Signal Processing
Gaussian processes (GPs) are versatile tools that have been successfully...
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Overlapping Mixtures of Gaussian Processes for the Data Association Problem
In this work we introduce a mixture of GPs to address the data associati...
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Steven Van Vaerenbergh
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