- Referenced in 473 articles
- AlexNet is a convolutional neural network that is 8 layers deep. You can load ... networks in MATLAB®, see Pretrained Deep Neural Networks...
- Referenced in 618 articles
- conducting machine learning and deep neural networks research, but the system is general enough...
- Referenced in 306 articles
- This package is for generating neural networks with many layers (deep architectures) and train them ... Reducing the dimensionality of data with neural networks” (G. E. Hinton, R. R. Salakhutdinov). This...
- Referenced in 414 articles
- PyTorch python package: Tensors and Dynamic neural networks in Python with strong GPU acceleration. PyTorch...
- Referenced in 293 articles
- application to the problem of training neural networks. Scatter search is an evolutionary method that ... optimal weight values in a multilayer neural network. Through experimentation, we show that our instantiation...
Neural Network Toolbox
- Referenced in 178 articles
- Neural Network Toolbox. Neural Network Toolbox™ provides functions and apps for modeling complex nonlinear systems ... modeled with a closed-form equation. Neural Network Toolbox supports supervised learning with feedforward, radial ... basis, and dynamic networks. It also supports unsupervised learning with self-organizing maps and competitive ... design, train, visualize, and simulate neural networks. You can use Neural Network Toolbox for applications...
- Referenced in 164 articles
- approximating the solution with a deep neural network which is trained to satisfy the differential ... Instead of forming a mesh, the neural network is trained on batches of randomly sampled ... with the solution approximated by a neural network instead of a linear combination of basis ... theorem regarding the approximation power of neural networks for a class of quasilinear parabolic PDEs...
- Referenced in 279 articles
- system implemented in the framework of adaptive networks. By using a hybrid learning procedure ... yielding remarkable results. Comparisons with artificial neural networks and earlier work on fuzzy modeling...
- Referenced in 183 articles
- Keras is a minimalist, highly modular neural networks library, written in Python and capable...
- Referenced in 106 articles
- link frequency assignment problem occurs when a network of radio links has to be established ... local search methods, genetic algorithms, neural networks, and potential reduction. These algorithms were tested...
- Referenced in 103 articles
- distance-based constructive learning algorithm Multi-layer networks of threshold logic units offer an attractive ... pattern classification systems. A new constructive neural network learning algorithm (DistAl) based on inter-pattern...
- Referenced in 63 articles
- Inspired by the latest development of neural network designs in deep learning, we propose ... feed-forward deep network, called PDE-Net, to fulfill two objectives at the same time ... learning convolution kernels (filters), and apply neural networks or other machine learning methods to approximate ... Network-In-Network (NIN) and Residual Neural Network (ResNet). Numerical experiments show that...
- Referenced in 53 articles
- present an overview of physics-informed neural networks (PINNs), which embed a PDE into ... loss of the neural network using automatic differentiation. The PINN algorithm is simple...
- Referenced in 64 articles
- Musicant, IEEE Trans. Neural Networks 10, 1032-1037 (1999)] and SMO [J. Platt, in: Advances...
- Referenced in 39 articles
- While it is widely known that neural networks are universal approximators of continuous functions ... more powerful result is that a neural network with a single hidden layer can approximate ... suggestive of the potential application of neural networks in learning nonlinear operators from data. However...
- Referenced in 60 articles
- environment, but the tools in its neural networks toolbox for SOM were not really...
- Referenced in 33 articles
- solver package which consists of neural network solvers for partial differential equations using scientific machine ... SciML) techniques such as physics-informed neural networks (PINNs) and deep BSDE solvers. This package ... utilizes deep neural networks and neural stochastic differential equations to solve high-dimensional PDEs...
- Referenced in 45 articles
- construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very...
- Referenced in 27 articles
- fPINNs: Fractional Physics-Informed Neural Networks. Physics-informed neural networks (PINNs) are effective in solving ... noisy data. PINNs employ standard feedforward neural networks (NNs) with the PDEs explicitly encoded into...
- Referenced in 43 articles
- design a novel type of neural network that directly consumes point clouds and well respects...