• PointNet

  • Referenced in 43 articles [sw31209]
  • design a novel type of neural network that directly consumes point clouds and well respects...
  • Brian

  • Referenced in 29 articles [sw23588]
  • 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...
  • BinaryConnect

  • Referenced in 24 articles [sw35871]
  • 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...
  • Reluplex

  • Referenced in 20 articles [sw31367]
  • 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...
  • BinaryNet

  • Referenced in 21 articles [sw35872]
  • Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained ... introduce a method to train Binarized Neural Networks (BNNs) - neural networks with binary weights...
  • DeepLab

  • Referenced in 38 articles [sw15303]
  • responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge...
  • DeepFool

  • Referenced in 21 articles [sw20937]
  • accurate method to fool deep neural networks. State-of-the-art deep neural networks have ... efficiently compute perturbations that fool deep networks, and thus reliably quantify the robustness of these ... accurate method to fool deep neural networks...
  • NSFnets

  • Referenced in 23 articles [sw42059]
  • Navier-Stokes Flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations ... employ physics-informed neural networks (PINNs) to simulate the incompressible flows ranging from laminar...
  • Xception

  • Referenced in 23 articles [sw39068]
  • interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between ... propose a novel deep convolutional neural network architecture inspired by Inception, where Inception modules have...
  • SSD

  • Referenced in 31 articles [sw26652]
  • images using a single deep neural network. Our approach, named SSD, discretizes the output space...
  • MobileNets

  • Referenced in 22 articles [sw39590]
  • MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. We present a class of efficient ... convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters...
  • LSTM

  • Referenced in 30 articles [sw03373]
  • human brain is a recurrent neural network (RNN): a network of neurons with feedback connections...
  • XNOR-Net

  • Referenced in 21 articles [sw39593]
  • ImageNet Classification Using Binary Convolutional Neural Networks. We propose two efficient approximations to standard convolutional ... neural networks: Binary-Weight-Networks and XNOR-Networks. In Binary-Weight-Networks, the filters...
  • PINNsNTK

  • Referenced in 17 articles [sw42058]
  • neural tangent kernel perspective. Physics-informed neural networks (PINNs) have lately received great attention thanks ... known about how such constrained neural networks behave during their training via gradient descent. More ... these questions through the lens of the Neural Tangent Kernel (NTK); a kernel that ... captures the behavior of fully-connected neural networks in the infinite width limit during training...
  • ART 3

  • Referenced in 27 articles [sw08755]
  • distributed pattern recognition codes in a neural network hierarchy is introduced. The search process functions...
  • SegNet

  • Referenced in 27 articles [sw27575]
  • novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed...
  • Evolino

  • Referenced in 19 articles [sw36450]
  • linear search for sequence learning. Current Neural Network learning algorithms are limited in their ability ... systems. Most supervised gradient-based recurrent neural networks (RNNs) suffer from a vanishing error signal...
  • NETT

  • Referenced in 15 articles [sw41773]
  • NETT: Solving Inverse Problems with Deep Neural Networks. Recovering a function or high-dimensional parameter ... novel algorithms using deep learning and neural networks for inverse problems appeared. While still ... regularizer defined by a trained neural network. We derive well-posedness results and quantitative error ... different from any previous work using neural networks for solving inverse problems. A possible data...
  • FUNFITS

  • Referenced in 26 articles [sw02191]
  • gotten from CRAN (www.cran.r-project.org). The rest -- neural networks, global and local Lyapunov exponents -- is here...
  • MatConvNet

  • Referenced in 18 articles [sw15651]
  • MatConvNet – convolutional neural networks for MATLAB. MatConvNet is an open source implementation of Convolutional Neural...