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Neural Network Applications [PDF]
Proceedings Electronic Technology Directions to the Year 2000, 1995 Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in ...
Jain, L.C., Veelenturf, L.P.J., Vonk, E.
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Explanations for Neural Networks by Neural Networks [PDF]
Applied Sciences, 2022 Understanding the function learned by a neural network is crucial in many domains, e.g., to detect a model’s adaption to concept drift in online learning. Existing global surrogate model approaches generate explanations by maximizing the fidelity between the neural network and a surrogate model on a sample-basis, which can be very time-consuming ...
Sascha Marton +2 more
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Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey [PDF]
, 2021 Dynamic networks are used in a wide range of fields, including social network analysis, recommender systems, and epidemiology. Representing complex networks as structures changing over time allow network models to leverage not only structural but also ...
Gabrys, Bogdan +2 more
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Persistence of shocks in CDS returns on Croatian bonds: Quantile autoregression approach [PDF]
Zbornik radova Ekonomskog fakulteta u Rijeci : časopis za ekonomsku teoriju i praksu, 2019 The paper aims to examine persistence of shocks in returns on CDS for 5Y Croatian bonds. Based on sample of daily data from January 6, 2004 up until December 13, 2019 the paper evaluated research hypothesis that assumed persistence ...
Mile Bošnjak, Ivan Novak, Maja Bašić
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Bootstrapping Neural Networks [PDF]
Neural Computation, 2000 Knowledge about the distribution of a statistical estimator is important for various purposes, such as the construction of confidence intervals for model parameters or the determination of critical values of tests. A widely used method to estimate this distribution is the so-called bootstrap, which is based on an imitation of the probabilistic ...
Franke, Jürgen, Neumann, Michael
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Bioelectronic Medicine, 2021 Background Extracellular recording represents a crucial electrophysiological technique in neuroscience for studying the activity of single neurons and neuronal populations.
Joshua J. Strohl +4 more
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Correlational Neural Networks [PDF]
Neural Computation, 2016 Common representation learning (CRL), wherein different descriptions (or views) of the data are embedded in a common subspace, has been receiving a lot of attention recently. Two popular paradigms here are canonical correlation analysis (CCA)–based approaches and autoencoder (AE)–based approaches.
Chandar, Sarath +3 more
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Interacting neural networks [PDF]
Physical Review E, 2000 Several scenarios of interacting neural networks which are trained either in an identical or in a competitive way are solved analytically. In the case of identical training each perceptron receives the output of its neighbour. The symmetry of the stationary state as well as the sensitivity to the used training algorithm are investigated.
Kinzel, W., Metzler, R., Kanter, I.
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Scientific Reports, 2021 Semantic categorization is a fundamental ability in language as well as in interaction with the environment. However, it is unclear what cognitive and neural basis generates this flexible and context dependent categorization of semantic information.
Atsushi Matsumoto +3 more
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Communications Biology, 2022 Modelling based on fMRI data obtained during more than 100 different cognitive tasks reveals that representation and decoding are preserved across the cortex, cerebellum, and ...
Tomoya Nakai, Shinji Nishimoto
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