Results 1 to 10 of about 1,641,434 (167)
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
openaire +2 more sources
Neural Networks With Motivation [PDF]
Frontiers in Systems Neuroscience, 2021 Animals rely on internal motivational states to make decisions. The role of motivational salience in decision making is in early stages of mathematical understanding. Here, we propose a reinforcement learning framework that relies on neural networks to learn optimal ongoing behavior for dynamically changing motivation values. First, we show that neural
Marcus Stephenson-Jones +5 more
openaire +5 more sources
Operational neural networks [PDF]
Neural Computing and Applications, 2020 AbstractFeed-forward, fully connected artificial neural networks or the so-called multi-layer perceptrons are well-known universal approximators. However, their learning performance varies significantly depending on the function or the solution space that they attempt to approximate. This is mainly because of their homogenous configuration based solely
Serkan Kiranyaz +3 more
openaire +6 more sources
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
core +2 more sources
Procedia Computer Science, 2023 Bayesian Neural Networks (BNNs) provide a tool to estimate the uncertainty of a neural network by considering a distribution over weights and sampling different models for each input. In this paper, we propose a method for uncertainty estimation in neural networks which, instead of considering a distribution over weights, samples outputs of each layer ...
Oleksiienko, Illia +2 more
openaire +4 more sources
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
Thermodynamic Neural Network [PDF]
Entropy, 2020 A thermodynamically motivated neural network model is described that self-organizes to transport charge associated with internal and external potentials while in contact with a thermal reservoir. The model integrates techniques for rapid, large-scale, reversible, conservative equilibration of node states and slow, small-scale, irreversible, dissipative
openaire +6 more sources