Results 21 to 30 of about 3,974,691 (210)
Stability and Synchronization of Switched Multi-Rate Recurrent Neural Networks
IEEE Access, 2021 Several designs of recurrent neural networks have been proposed in the literature involving different clock times. However, the stability and synchronization of this kind of system have not been studied.
Victoria Ruiz +3 more
doaj +1 more source
Deductron—A Recurrent Neural Network [PDF]
Frontiers in Applied Mathematics and Statistics, 2020 34 pages, contains Python code, Python code requires data file data.py (should be included in the archive)
openaire +5 more sources
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
Fusion Recurrent Neural Network
CoRR, 2020 Considering deep sequence learning for practical application, two representative RNNs - LSTM and GRU may come to mind first. Nevertheless, is there no chance for other RNNs? Will there be a better RNN in the future? In this work, we propose a novel, succinct and promising RNN - Fusion Recurrent Neural Network (Fusion RNN).
Yiwen Sun +5 more
openaire +3 more sources
Deep Sparse Learning for Automatic Modulation Classification Using Recurrent Neural Networks
Sensors, 2021 Deep learning models, especially recurrent neural networks (RNNs), have been successfully applied to automatic modulation classification (AMC) problems recently.
Ke Zang, Wenqi Wu, Wei Luo
doaj +1 more source
SORN : a self-organizing recurrent neural network [PDF]
, 2009 Understanding the dynamics of recurrent neural networks is crucial for explaining how the brain processes information. In the neocortex, a range of different plasticity mechanisms are shaping recurrent networks into effective information processing ...
Pipa, Gordon +5 more
core +1 more source
A Deep Multi-Task Learning Approach for Bioelectrical Signal Analysis
Mathematics, 2023 Deep learning is a promising technique for bioelectrical signal analysis, as it can automatically discover hidden features from raw data without substantial domain knowledge.
Jishu K. Medhi +3 more
doaj +1 more source
Quasi-Recurrent Neural Networks
CoRR, 2016 Submitted to conference track at ICLR ...
BRADBURY JAMES +3 more
openaire +5 more sources
Prediction of Convergence Dynamics of Design Performance using Differential Recurrent Neural Networks [PDF]
, 2008 Computational Fluid Dynamics (CFD) simulations have been extensively used in many aerodynamic design optimization problems, such as wing and turbine blade shape design optimization.
Sendhoff, Bernhard +12 more
core +1 more source