Results 21 to 30 of about 296,489 (278)

On the quantization of recurrent neural networks

CoRR, 2021
Integer quantization of neural networks can be defined as the approximation of the high precision computation of the canonical neural network formulation, using reduced integer precision. It plays a significant role in the efficient deployment and execution of machine learning (ML) systems, reducing memory consumption and leveraging typically faster ...
Jian Li, Raziel Alvarez
openaire   +2 more sources

Complexity without chaos: Plasticity within random recurrent networks generates robust timing and motor control [PDF]

, 2012
It is widely accepted that the complex dynamics characteristic of recurrent neural circuits contributes in a fundamental manner to brain function. Progress has been slow in understanding and exploiting the computational power of recurrent dynamics for ...
A Banerjee, A Litwin-Kumar, C Miall, C van Vreeswijk, CA Skarda, CV Buhusi, D Bueti, D Durstewitz, D Durstewitz, D Sussillo, DA Crowe, Dean V Buonomano, DJ Watts, DV Buonomano, DV Buonomano, DV Buonomano, DV Buonomano, DV Buonomano, E Pastalkova, EM Izhikevich, G Boffetta, G Fagiolo, H Jaeger, H Jaeger, H Kantz, H Merchant, H Sompolinsky, J Coull, JF Medina, JF Medina, JJ Hopfield, JK Liu, JX Li, K Rajan, M London, M Monteforte, M Rabinovich, MA Long, MB Ahrens, MD Mauk, MM Churchland, MS Goldman, MS Matell, N Brunel, P Janssen, P Simen, RB Ivry, RB Ivry, RHR Hahnloser, RM Church, Rodrigo Laje, S Ganguli, S Song, XJ Wang   +53 more
core   +2 more sources

Medical Image Interpolation Using Recurrent Type-2 Fuzzy Neural Network

Frontiers in Neuroinformatics, 2021
Image interpolation is an essential process for image processing and computer graphics in wide applications to medical imaging. For image interpolation used in medical diagnosis, the two-dimensional (2D) to three-dimensional (3D) transformation can ...
Jafar Tavoosi, Chunwei Zhang, Ardashir Mohammadzadeh, Saleh Mobayen, Amir H. Mosavi, Amir H. Mosavi   +5 more
doaj   +1 more source

Anti-Periodic Synchronization of Clifford-Valued Neutral-Type Recurrent Neural Networks With D Operator

IEEE Access, 2022
In this paper, a class of Clifford-valued neutral-type recurrent neural networks with $D$ operator is explored. By using non-decomposition method and the Banach fixed point theorem, we obtain several sufficient conditions for the existence of anti ...
Jin Gao, Lihua Dai
doaj   +1 more source

Application of Recurrent Neural Networks to Model Bias Correction: Idealized Experiments With the Lorenz‐96 Model

Journal of Advances in Modeling Earth Systems, 2023
Systematic biases in numerical weather prediction models cause forecast deviation from reality. While model biases also affect data assimilation and degrade the analysis accuracy, observation information incorporated through data assimilation can provide
A. Amemiya, M. Shlok, T. Miyoshi
doaj   +1 more source

Real-Time Hardware Identification of Complex Dynamical Plant by Artificial Neural Network Based on Experimentally Processed Data by Smart Technologies

Engineering Proceedings, 2023
Artificial neural networks with different structures are used for identification of complex dynamic plant with distributed parameters. The plant is a high-temperature plasma in the spherical Globus-M2 tokamak.
Valerii I. Kruzhkov, Yuri V. Mitrishkin, Eugenia A. Pavlova   +2 more
doaj   +1 more source

Quasi-Recurrent Neural Networks

CoRR, 2016
Submitted to conference track at ICLR ...
James Bradbury 0002, Stephen Merity, Caiming Xiong, Richard Socher   +3 more
openaire   +3 more sources

Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification

IEEE Access, 2021
A novel and efficient end-to-end learning model for automatic modulation classification is proposed for wireless spectrum monitoring applications, which automatically learns from the time domain in-phase and quadrature data without requiring the design ...
Kaisheng Liao, Yaodong Zhao, Jie Gu, Yaping Zhang, Yi Zhong   +4 more
doaj   +1 more source

Random Recurrent Neural Networks Dynamics [PDF]

, 2006
This paper is a review dealing with the study of large size random recurrent neural networks. The connection weights are selected according to a probability law and it is possible to predict the network dynamics at a macroscopic scale using an averaging ...
Cessac, B., Samuelides, M.
core   +2 more sources

Bayesian Recurrent Neural Networks

CoRR, 2017
In this work we explore a straightforward variational Bayes scheme for Recurrent Neural Networks. Firstly, we show that a simple adaptation of truncated backpropagation through time can yield good quality uncertainty estimates and superior regularisation at only a small extra computational cost during training, also reducing the amount of parameters by
Meire Fortunato, Charles Blundell, Oriol Vinyals   +2 more
openaire   +2 more sources