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Found 14 papers, 2 papers with code
A Classification of Artificial Intelligence Systems for Mathematics Education
This chapter provides an overview of the different Artificial Intelligence (AI) systems that are being used in contemporary digital tools for Mathematics Education (ME).
On the Stability and Generalization of Learning with Kernel Activation Functions
In this brief we investigate the generalization properties of a recently-proposed class of non-parametric activation functions, the kernel activation functions (KAFs).
Widely Linear Kernels for Complex-Valued Kernel Activation Functions
Complex-valued neural networks (CVNNs) have been shown to be powerful nonlinear approximators when the input data can be properly modeled in the complex domain.
Recurrent Neural Networks with Flexible Gates using Kernel Activation Functions
Gated recurrent neural networks have achieved remarkable results in the analysis of sequential data.
Improving Graph Convolutional Networks with Non-Parametric Activation Functions
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.
Complex-valued Neural Networks with Non-parametric Activation Functions
Complex-valued neural networks (CVNNs) are a powerful modeling tool for domains where data can be naturally interpreted in terms of complex numbers.
Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space
In this paper, we study the problem of locating a predefined sequence of patterns in a time series.
Kafnets: kernel-based non-parametric activation functions for neural networks
Neural networks are generally built by interleaving (adaptable) linear layers with (fixed) nonlinear activation functions.
Recursive Multikernel Filters Exploiting Nonlinear Temporal Structure
In kernel methods, temporal information on the data is commonly included by using time-delayed embeddings as inputs.
On the Relationship between Online Gaussian Process Regression and Kernel Least Mean Squares Algorithms
We study the relationship between online Gaussian process (GP) regression and kernel least mean squares (KLMS) algorithms.
Bayesian Extensions of Kernel Least Mean Squares
The kernel least mean squares (KLMS) algorithm is a computationally efficient nonlinear adaptive filtering method that "kernelizes" the celebrated (linear) least mean squares algorithm.
Gaussian Processes for Nonlinear Signal Processing
Gaussian processes (GPs) are versatile tools that have been successfully employed to solve nonlinear estimation problems in machine learning, but that are rarely used in signal processing.
Overlapping Mixtures of Gaussian Processes for the Data Association Problem
In this work we introduce a mixture of GPs to address the data association problem, i. e. to label a group of observations according to the sources that generated them.
