• TensorFlow

  • Referenced in 653 articles [sw15170]
  • purposes of conducting machine learning and deep neural networks research, but the system is general...
  • AlexNet

  • Referenced in 542 articles [sw38522]
  • AlexNet is a convolutional neural network that is 8 layers deep. You can load ... pretrained networks in MATLAB®, see Pretrained Deep Neural Networks...
  • DGM

  • Referenced in 185 articles [sw39282]
  • 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 ... Jacobi-Bellman PDE and Burgers’ equation. The deep learning algorithm approximates the general solution ... with the solution approximated by a neural network instead of a linear combination of basis...
  • darch

  • Referenced in 321 articles [sw11086]
  • package is for generating neural networks with many layers (deep architectures) and train them with ... publications ”A fast learning algorithm for deep belief nets” (G. E. Hinton, S. Osindero ... Reducing the dimensionality of data with neural networks” (G. E. Hinton, R. R. Salakhutdinov). This...
  • PyTorch

  • Referenced in 440 articles [sw20939]
  • Dynamic neural networks in Python with strong GPU acceleration. PyTorch is a deep learning framework...
  • NeuralPDE.jl

  • Referenced in 38 articles [sw39548]
  • informed neural networks (PINNs) and deep BSDE solvers. This package utilizes deep neural networks...
  • PDE-Net

  • Referenced in 91 articles [sw36963]
  • latest development of neural network designs in deep learning, we propose a new feed-forward ... deep network, called PDE-Net, to fulfill two objectives at the same time: to accurately ... learning convolution kernels (filters), and apply neural networks or other machine learning methods to approximate...
  • Reluplex

  • Referenced in 20 articles [sw31367]
  • Efficient SMT Solver for Verifying Deep Neural Networks. Deep neural networks have emerged ... efficient technique for verifying properties of deep neural networks (or providing counter-examples). The technique ... neural networks. The verification procedure tackles neural networks as a whole, without making any simplifying ... evaluated our technique on a prototype deep neural network implementation of the next-generation airborne...
  • Keras

  • Referenced in 210 articles [sw15491]
  • Deep Learning library for Theano and TensorFlow. Keras is a minimalist, highly modular neural networks ... research. Use Keras if you need a deep learning library that: allows for easy...
  • BinaryConnect

  • Referenced in 24 articles [sw35871]
  • BinaryConnect: Training Deep Neural Networks with binary weights during propagations. Deep Neural Networks (DNN) have ... research and development of dedicated hardware for Deep Learning (DL). Binary weights, i.e., weights which ... components of the digital implementation of neural networks. We introduce BinaryConnect, a method which consists...
  • DeepFool

  • Referenced in 21 articles [sw20937]
  • simple and accurate method to fool deep neural networks. State-of-the-art deep neural ... robustness of state-of-the-art deep classifiers to such perturbations on large-scale datasets ... efficiently compute perturbations that fool deep networks, and thus reliably quantify the robustness of these ... simple and accurate method to fool deep neural networks...
  • SSD

  • Referenced in 34 articles [sw26652]
  • objects in images using a single deep neural network. Our approach, named SSD, discretizes...
  • DeepFace

  • Referenced in 25 articles [sw21625]
  • face representation from a nine-layer deep neural network. This deep network involves more than...
  • BinaryNet

  • Referenced in 21 articles [sw35872]
  • Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained...
  • MobileNets

  • Referenced in 28 articles [sw39590]
  • MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. We present a class of efficient ... separable convolutions to build light weight deep neural networks. We introduce two simple global hyper...
  • WaveNet

  • Referenced in 26 articles [sw38795]
  • Audio. This paper introduces WaveNet, a deep neural network for generating raw audio waveforms...
  • DeepLab

  • Referenced in 39 articles [sw15303]
  • feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively...
  • NETT

  • Referenced in 18 articles [sw41773]
  • NETT: Solving Inverse Problems with Deep Neural Networks. Recovering a function or high-dimensional parameter ... understood. Recently, novel algorithms using deep learning and neural networks for inverse problems appeared. While ... However, there are few theoretical results for deep learning in inverse problems. In this paper ... complete convergence analysis for the proposed NETT (Network Tikhonov) approach to inverse problems. NETT considers...
  • SciANN

  • Referenced in 16 articles [sw38344]
  • computations and physics-informed deep learning using artificial neural networks. In this paper, we introduce ... computing and physics-informed deep learning using artificial neural networks. SciANN uses the widely used ... packages TensorFlow and Keras to build deep neural networks and optimization models, thus inheriting many...
  • Entropy-SGD

  • Referenced in 21 articles [sw41231]
  • algorithm called Entropy-SGD for training deep neural networks that is motivated by the local...