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...G |first=Diganta |last=Misra |title=Mish: A Self Regularized Non-Monotonic Neural Activation Function |date=2019}}</ref> SiLU was first proposed alongside the [[Rectifier (neural networks)|GELU]] in 2016,<ref name="Hendrycks-Gimpel_2016" /> then again pr ...

{{Short description|Architectural motif in neural networks for controlling information flow}} ...pagation|gradient signals]]. They are most prominently used in [[recurrent neural network]]s (RNNs), but have also found applications in other architectures. ...

{{short description|Memory unit used in neural networks}} ...web.archive.org/web/20211110112626/http://www.wildml.com/2015/10/recurrent-neural-network-tutorial-part-4-implementing-a-grulstm-rnn-with-python-and-theano/ ...

{{Short description|Type of artificial neural network}} ...es]] ("hidden units"), with connections between the layers but not between units within each layer.<ref name="scholar">{{Cite journal | vauthors = Hinton G ...

{{Short description|THE LOTTERY TICKET HYPOTHESIS: FINDING SPARSE, TRAINABLE NEURAL NETWORKS}} ...1=Jonathan |title=The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks |date=2019-03-04 |eprint=1803.03635 |last2=Carbin |first2=Michael| ...

...ations in mathematics, physics, and engineering, particularly in '''signal processing''', '''image enhancement''', and '''Fourier analysis''', where it provides The traditional quaternion algebra is based on imaginary units <math> \mathbf{i, j, k} </math> that satisfy the relations: ...

...e distribution over functions corresponding to an infinitely wide Bayesian neural network.}} ...iety of network architectures converges to a GP in [[Large width limits of neural networks|the infinitely wide limit]], [[convergence in distribution|in the ...

...ptual organization|perceptual organisation]] which asks for the functional units and elementary features that are relevant for a [[perceptual system]] in th * [[Neural processing for individual categories of objects]] ...

{{Short description|Class of artificial neural network}} {{Machine learning|Artificial neural network}} ...

...learningmastery.com/rectified-linear-activation-function-for-deep-learning-neural-networks/ |website=Machine Learning Mastery |access-date=8 April 2021 |date ...t3 = Abbott |first3 = L. F. |title = Lyapunov spectra of chaotic recurrent neural networks |date = 2020-06-03 |class = nlin.CD |eprint=2006.02427}}</ref> ...

...e]]).<ref name="Hinton15">{{cite arXiv|title=Distilling the knowledge in a neural network|year=2015|eprint=1503.02531|last1=Hinton|first1=Geoffrey|last2=Viny ...s|conference=IEEE International Conference on Acoustics, Speech and Signal Processing|pages=4825–4829|year=2017}}</ref> ...

{{distinguish|text=[[Bayesian neural network|Bayesian Neural Network (BNN)]]}} {{Short description|Artificial neural network}} ...

{{short description|Convolutional neural network structure}} '''LeNet''' is a series of [[convolutional neural network]] architectures created by a research group in [[AT&T Bell Laborato ...

.../ieeexplore.ieee.org/document/7891546 |journal=IEEE Transactions on Signal Processing |volume=65 |issue=13 |pages=3551–3582 |doi=10.1109/TSP.2017.2690524 |arxiv= ...nal=Proceedings of the 33rd International Conference on Neural Information Processing Systems| year=2019| pages=8026–037| arxiv=1912.01703}}</ref><ref name="adab ...

...cription|Technique for setting initial values of trainable parameters in a neural network}} ...step in creating a [[Neural network (machine learning)|neural network]]. A neural network contains trainable parameters that are modified during training: we ...

In [[signal processing]], '''Feature space Maximum Likelihood Linear Regression''' ('''fMLLR''') i ...ce on Affective Computing and Intelligent Interaction |chapter=Hybrid Deep Neural Network--Hidden Markov Model (DNN-HMM) Based Speech Emotion Recognition |da ...

...">Nelles O. "Nonlinear System Identification: From Classical Approaches to Neural Networks". Springer Verlag, 2001</ref><ref name="SAB1">Billings S.A. "Nonli # [[Neural network]] models, ...

...Training Deep Neural Networks for False Alarm Detection in Intensive Care Units |date=2018 |pages=1157–1161 |doi=10.23919/EUSIPCO.2018.8552944 |isbn=978-9- ...

=== Information processing === ...anticscholar.org/09d6/5e055dfae5b02b1ffe570a920cc6e99e705a.pdf Information Processing in Single Cells and Small Networks: Insights from Compartmental Models]. In ...

...eptember 2020|title=Gradient amplification: An efficient way to train deep neural networks|journal=Big Data Mining and Analytics|volume=3|issue=3|pages=198|d ...irst3=Yoshua|date=2012-11-21|title=On the difficulty of training Recurrent Neural Networks|eprint=1211.5063|class=cs.LG}}</ref> The latter are trained by unf ...