• PDE-Net

  • Referenced in 63 articles [sw36963]
  • learning convolution kernels (filters), and apply neural networks or other machine learning methods to approximate ... expressive and predictive power of the network. These constrains are carefully designed by fully exploiting ... existing networks in computer vision such as Network-In-Network (NIN) and Residual Neural Network...
  • DnCNN

  • Referenced in 45 articles [sw39678]
  • Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising. Discriminative model learning ... construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very ... regularization method into image denoising. Specifically, residual learning and batch normalization are utilized to speed...
  • deep_speck

  • Referenced in 2 articles [sw34578]
  • present distinguishers based on deep residual neural networks that achieve a mean key rank roughly...
  • DeepXDE

  • Referenced in 61 articles [sw32456]
  • into the loss of the neural network using automatic differentiation. The PINN algorithm is simple ... forward problems. We propose a new residual-based adaptive refinement (RAR) method to improve...
  • FPINNs

  • Referenced in 27 articles [sw40570]
  • noisy data. PINNs employ standard feedforward neural networks (NNs) with the PDEs explicitly encoded into ... mean-squared PDE-residuals and the mean-squared error in initial/boundary conditions is minimized with...
  • CVDNet

  • Referenced in 1 article [sw41772]
  • propose CVDNet, a Deep Convolutional Neural Network (CNN) model to classify COVID-19 infection from ... proposed architecture is based on the residual neural network and it is constructed by using...
  • GNMT

  • Referenced in 28 articles [sw26579]
  • this work, we present GNMT, Google’s Neural Machine Translation system, which attempts to address ... LSTM network with 8 encoder and 8 decoder layers using attention and residual connections...
  • ERAN

  • Referenced in 1 article [sw40545]
  • verify safety properties of neural networks with feedforward, convolutional, and residual layers against input perturbations...
  • SPINE-D

  • Referenced in 1 article [sw16912]
  • Long Disordered Regions by a Single Neural-Network Based Method. Short and long disordered regions ... have different preference for different amino acid residues. Different methods often have to be trained ... this study, we developed a single neural-network-based technique called SPINE-D that makes ... three-state prediction first (ordered residues and disordered residues in short and long disordered regions...
  • NNcon

  • Referenced in 6 articles [sw17013]
  • contact map prediction using 2D-recursive neural networks. Protein contact map prediction is useful ... NNcon was ranked among the most accurate residue contact predictors in the Eighth Critical Assessment...
  • TUNE

  • Referenced in 1 article [sw35757]
  • protein sequence–structure compatibility. An artificial neural network model is trained to predict compatibility ... decoy protein 3D structures. With a residue level structural description, its performance is comparable ... with residue level structural descriptions. Availability: The C++ source code of our neural network model...
  • VarNet

  • Referenced in 9 articles [sw42184]
  • partial differential equations (PDEs) using deep neural networks (NNs). Particularly, we propose a novel loss ... feedback provided from the PDE residual. The models obtained using VarNet are smooth...
  • GNBSL

  • Referenced in 2 articles [sw26874]
  • composition, and segment composition. Four probabilistic neural network (PNN) classifiers are used to classify these ... this system. One module extracts the residue-couple distribution from the amino acid sequence...
  • QuartzNet

  • Referenced in 2 articles [sw38353]
  • propose a new end-to-end neural acoustic model for automatic speech recognition. The model ... composed of multiple blocks with residual connections between them. Each block consists ... trained with CTC loss. The proposed network achieves near state-of-the-art accuracy...
  • TMBETA-NET

  • Referenced in 2 articles [sw11339]
  • mainly based on feed forward neural network and refined with β-strand length. Our program ... stretch of highlighted amino acid residues. Further, the probability of residues to be in transmembrane...
  • PVANet

  • Referenced in 4 articles [sw28335]
  • PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection. This paper presents ... network is deep and thin and trained with the help of batch normalization, residual connections...
  • QAcon

  • Referenced in 1 article [sw34249]
  • residue contact predictions. We apply residue-residue contact information predicted by two protein contact prediction ... input to train a two-layer neural network on CASP9 datasets to predict the quality...
  • PONDR-FIT

  • Referenced in 2 articles [sw16904]
  • Here we introduced a consensus artificial neural network (ANN) prediction method, which was developed ... short disordered regions with less than ten residues, as well as for the residues close...
  • YOLOv4

  • Referenced in 1 article [sw40822]
  • which are said to improve Convolutional Neural Network (CNN) accuracy. Practical testing of combinations ... some features, such as batch-normalization and residual-connections, are applicable to the majority...
  • TFLearn

  • Referenced in 1 article [sw21054]
  • prototyping through highly modular built-in neural network layers, regularizers, optimizers, metrics... Full transparency over ... such as Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, Generative networks... In the future, TFLearn...