Zobrazeno 1 - 10
of 103
pro vyhledávání: '"Van Vaerenbergh, Steven"'
Autor:
Vermani, Ayesha, Dowling, Matthew, Jeon, Hyungju, Jordan, Ian, Nassar, Josue, Bernaerts, Yves, Zhao, Yuan, Van Vaerenbergh, Steven, Park, Il Memming
Function and dysfunctions of neural systems are tied to the temporal evolution of neural states. The current limitations in showing their causal role stem largely from the absence of tools capable of probing the brain's internal state in real-time. T
Externí odkaz:
http://arxiv.org/abs/2409.01280
Publikováno v:
In Mechanical Systems and Signal Processing 1 September 2024 218
Autor:
Arias-Pastor, Mercedes1 (AUTHOR) mercedes.ariaspastor@unican.es, Van Vaerenbergh, Steven2 (AUTHOR) steven.vanvaerenbergh@unican.es, González-Bernal, Jerónimo J.3 (AUTHOR) jejavier@ubu.es, González-Santos, Josefa3 (AUTHOR) mjgonzalez@ubu.es
Publikováno v:
Behavioral Sciences (2076-328X). Jul2024, Vol. 14 Issue 7, p563. 30p.
This chapter provides an overview of the different Artificial Intelligence (AI) systems that are being used in contemporary digital tools for Mathematics Education (ME). It is aimed at researchers in AI and Machine Learning (ML), for whom we shed som
Externí odkaz:
http://arxiv.org/abs/2107.06015
In this brief we investigate the generalization properties of a recently-proposed class of non-parametric activation functions, the kernel activation functions (KAFs). KAFs introduce additional parameters in the learning process in order to adapt non
Externí odkaz:
http://arxiv.org/abs/1903.11990
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. One of the major challenges in scaling up CVNNs in practice is the design of complex acti
Externí odkaz:
http://arxiv.org/abs/1902.02085
Autor:
Scardapane, Simone, Van Vaerenbergh, Steven, Comminiello, Danilo, Totaro, Simone, Uncini, Aurelio
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 pap
Externí odkaz:
http://arxiv.org/abs/1807.04065
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 pr
Externí odkaz:
http://arxiv.org/abs/1802.09405
Complex-valued neural networks (CVNNs) are a powerful modeling tool for domains where data can be naturally interpreted in terms of complex numbers. However, several analytical properties of the complex domain (e.g., holomorphicity) make the design o
Externí odkaz:
http://arxiv.org/abs/1802.08026
In this paper, we study the problem of locating a predefined sequence of patterns in a time series. In particular, the studied scenario assumes a theoretical model is available that contains the expected locations of the patterns. This problem is fou
Externí odkaz:
http://arxiv.org/abs/1802.05910