• SGDR

  • Referenced in 17 articles [sw30752]
  • improve its anytime performance when training deep neural networks. We empirically study its performance...
  • Xception

  • Referenced in 23 articles [sw39068]
  • Xception: Deep Learning with Depthwise Separable Convolutions. We present ... 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...
  • NICE

  • Referenced in 15 articles [sw29631]
  • building blocks, each based on a deep neural network. The training criterion is simply...
  • SciANN

  • Referenced in 10 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...
  • SegNet

  • Referenced in 27 articles [sw27575]
  • present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise...
  • DeepSurv

  • Referenced in 7 articles [sw41011]
  • system using a Cox proportional hazards deep neural network. Methods: We introduce DeepSurv ... proportional hazards deep neural network and state-of-the-art survival method for modeling interactions ... will enable medical researchers to use deep neural networks as a tool in their exploration...
  • Marabou

  • Referenced in 6 articles [sw31368]
  • Framework for Verification and Analysis of Deep Neural Networks. Deep neural networks are revolutionizing ... pressing need for tools and techniques for network analysis and certification. To help in addressing ... present Marabou, a framework for verifying deep neural networks. Marabou is an SMT-based tool...
  • mixup

  • Referenced in 11 articles [sw35857]
  • mixup: Beyond Empirical Risk Minimization. Large deep neural networks are powerful, but exhibit undesirable behaviors...
  • DnCNN

  • Referenced in 45 articles [sw39678]
  • Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising. Discriminative model learning ... denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm...
  • VarNet

  • Referenced in 9 articles [sw42184]
  • VarNet: Variational Neural Networks for the Solution of Partial Differential Equations. In this paper ... partial differential equations (PDEs) using deep neural networks (NNs). Particularly, we propose a novel loss...
  • h2o

  • Referenced in 9 articles [sw17104]
  • gradient boosting machines, random forests, and neural networks (deep learning) within various cluster environments...
  • DeepPPI

  • Referenced in 4 articles [sw30745]
  • Prediction of Protein-Protein Interactions with Deep Neural Networks. The complex language of eukaryotic gene ... using a recent machine learning advance-deep neural networks (DNNs). We aim at improving ... propose a method called DeepPPI (Deep neural networks for Protein-Protein Interactions prediction), which employs ... deep neural networks to learn effectively the representations of proteins from common protein descriptors...
  • SchNetPack

  • Referenced in 5 articles [sw25772]
  • development and application of deep neural networks to the prediction of potential energy surfaces ... contains basic building blocks of atomistic neural networks, manages their training and provides simple access ... atomcentered symmetry functions and the deep tensor neural network SchNet as well as ready ... PyTorch deep learning framework, SchNetPack allows to efficiently apply the neural networks to large datasets...
  • GPipe

  • Referenced in 6 articles [sw39466]
  • Networks using Pipeline Parallelism. Scaling up deep neural network capacity has been known...
  • DeepID3

  • Referenced in 4 articles [sw39588]
  • DeepID3: Face Recognition with Very Deep Neural Networks. The state-of-the-art of face ... emergence of deep learning. Very deep neural networks recently achieved great success on general object ... recognition. This paper proposes two very deep neural network architectures, referred to as DeepID3...
  • AutoKeras

  • Referenced in 7 articles [sw33648]
  • been proposed to automatically tune deep neural networks, but existing search algorithms, e.g., NASNet, PNAS...
  • OpenFace

  • Referenced in 6 articles [sw32734]
  • open source face recognition with deep neural networks...
  • MatConvNet

  • Referenced in 18 articles [sw15651]
  • source implementation of Convolutional Neural Networks (CNNs) with a deep integration in the MATLAB environment...
  • TopologyNet

  • Referenced in 7 articles [sw41013]
  • TopologyNet: Topology based deep convolutional neural networks for biomolecular property predictions. Although deep learning approaches ... further integrate ESPH and convolutional neural networks to construct a multichannel topological neural network (TopologyNet ... upon mutation. To overcome the limitations to deep learning arising from small and noisy training ... present a multitask topological convolutional neural network (MT-TCNN). We demonstrate that the present TopologyNet...
  • BOHB

  • Referenced in 5 articles [sw35481]
  • Efficient Hyperparameter Optimization at Scale. Modern deep learning methods are very sensitive to many hyperparameters ... machines, feed-forward neural networks, Bayesian neural networks, deep reinforcement learning, and convolutional neural networks...