• Neural Network Toolbox

  • Referenced in 178 articles [sw07378]
  • 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...
  • DGM

  • Referenced in 152 articles [sw39282]
  • neural network instead of a linear combination of basis functions. In addition, we prove ... theorem regarding the approximation power of neural networks for a class of quasilinear parabolic PDEs...
  • ANFIS

  • Referenced in 279 articles [sw08730]
  • system implemented in the framework of adaptive networks. By using a hybrid learning procedure ... ANFIS architecture is employed to model nonlinear functions, identify nonlinear components on-line ... yielding remarkable results. Comparisons with artificial neural networks and earlier work on fuzzy modeling...
  • RONN

  • Referenced in 7 articles [sw16909]
  • RONN: the bio-basis function neural network technique applied to the detection of natively disordered ... regions is important both for understanding protein function and for facilitating structural analysis since disordered ... have developed the regional order neural network (RONN) software as an application ... recently developed ‘bio-basis function neural network’ pattern recognition algorithm to the detection of natively...
  • NETT

  • Referenced in 15 articles [sw41773]
  • Solving Inverse Problems with Deep Neural Networks. Recovering a function or high-dimensional parameter vector ... novel algorithms using deep learning and neural networks for inverse problems appeared. While still...
  • ART 3

  • Referenced in 27 articles [sw08755]
  • codes in a neural network hierarchy is introduced. The search process functions well with either...
  • MERLIN

  • Referenced in 14 articles [sw04248]
  • frequently reduced to ones of minimizing a function of many variables. For instance we refer ... training of neural networks. Minimizing a multidimensional function faces a lot of difficulties. There...
  • Reluplex

  • Referenced in 20 articles [sw31367]
  • technique for verifying properties of deep neural networks (or providing counter-examples). The technique ... ReLU) activation function, which is a crucial ingredient in many modern neural networks. The verification...
  • AutoKeras

  • Referenced in 6 articles [sw33648]
  • been proposed to automatically tune deep neural networks, but existing search algorithms, e.g., NASNet, PNAS ... Network morphism, which keeps the functionality of a neural network while changing its neural architecture ... framework develops a neural network kernel and a tree-structured acquisition function optimization algorithm...
  • SciANN

  • Referenced in 10 articles [sw38344]
  • deep neural networks and optimization models, thus inheriting many of Keras’s functionalities, such ... learning. SciANN is designed to abstract neural network construction for scientific computations and solution ... informed neural networks (PINN) architecture, therefore providing the flexibility to set up complex functional forms...
  • DiffEqFlux

  • Referenced in 7 articles [sw27559]
  • show high-level functionality for defining neural ordinary differential equations (neural networks embedded into...
  • ALEC

  • Referenced in 3 articles [sw02260]
  • optimizing neural networks wherein the neural network architecture, activation function, weights and learning algorithms ... performance of ALEC and artificial neural networks for function approximation problems. To evaluate the comparative ... generalization performance of four different neural network-learning algorithms. Performances of the different learning algorithms ... inevitable is ALEC to design a neural network, which is smaller, faster and with...
  • GELUs

  • Referenced in 5 articles [sw36443]
  • Unit (GELU), a high-performing neural network activation function. The GELU activation function...
  • Net2Net

  • Referenced in 5 articles [sw38088]
  • often trains very many different neural networks during the experimentation and design process. This ... from a previous network to each new deeper or wider network. Our techniques are based ... concept of function-preserving transformations between neural network specifications. This differs from previous approaches ... that altered the function represented by a neural net when adding layers to it. Using...
  • EAST

  • Referenced in 3 articles [sw28334]
  • concentrating efforts on designing loss functions and neural network architecture. Experiments on standard datasets including...
  • FuncNN

  • Referenced in 1 article [sw35064]
  • FuncNN: Functional Neural Networks. A collection of functions which fit functional neural network models ... build deep learning models that have either functional or scalar responses paired with functional...
  • SOM

  • Referenced in 60 articles [sw07375]
  • environment, but the tools in its neural networks toolbox for SOM were not really ... offer a simple, well documented MATLAB function package which is easy to use and modify...
  • NNV

  • Referenced in 3 articles [sw32539]
  • properties of feed-forward neural networks (FFNNs) with various activation functions. For learning-enabled ... closed-loop control systems incorporating neural networks, NNV provides exact and over-approximate reachability analysis ... piecewise-linear activation functions, such as ReLUs. For similar neural network control systems (NNCS) that...
  • TensorLog

  • Referenced in 6 articles [sw32913]
  • queries are compiled into differentiable functions in a neural-network infrastructure such as Tensorflow...
  • FUNFITS

  • Referenced in 26 articles [sw02191]
  • version of S-plus. Most of its functionality has been incorporated into the fields package ... gotten from CRAN (www.cran.r-project.org). The rest -- neural networks, global and local Lyapunov exponents -- is here...