- Referenced in 372 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 419 articles
- conducting machine learning and deep neural networks research, but the system is general enough...
- Referenced in 290 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...
- Referenced in 275 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...
Neural Network Toolbox
- Referenced in 175 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 271 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 263 articles
- PyTorch python package: Tensors and Dynamic neural networks in Python with strong GPU acceleration. PyTorch...
- Referenced in 137 articles
- Keras is a minimalist, highly modular neural networks library, written in Python and capable...
- Referenced in 63 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 105 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 100 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 61 articles
- Musicant, IEEE Trans. Neural Networks 10, 1032-1037 (1999)] and SMO [J. Platt, in: Advances...
- Referenced in 60 articles
- environment, but the tools in its neural networks toolbox for SOM were not really...
- Referenced in 36 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 27 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 25 articles
- Brian: a simulator for spiking neural networks in python. ”Brian” is a new simulator ... spiking neural networks, written in Python (http://brian. di.ens.fr). It is an intuitive and highly...
- Referenced in 18 articles
- BinaryConnect: Training Deep Neural Networks with binary weights during propagations. Deep Neural Networks (DNN) have ... components of the digital implementation of neural networks. We introduce BinaryConnect, a method which consists...
- Referenced in 29 articles
- design a novel type of neural network that directly consumes point clouds and well respects...
- Referenced in 29 articles
- construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very...
- Referenced in 14 articles
- Efficient SMT Solver for Verifying Deep Neural Networks. Deep neural networks have emerged ... technique for verifying properties of deep neural networks (or providing counter-examples). The technique ... crucial ingredient in many modern neural networks. The verification procedure tackles neural networks ... technique on a prototype deep neural network implementation of the next-generation airborne collision avoidance...