• BinaryConnect

  • Referenced in 24 articles [sw35871]
  • with binary weights during propagations. Deep Neural Networks (DNN) have achieved state ... research and development of dedicated hardware for Deep Learning (DL). Binary weights, i.e., weights which ... neural networks. We introduce BinaryConnect, a method which consists in training a DNN with binary...
  • Julius

  • Referenced in 5 articles [sw21656]
  • Recent version also supports Deep Neural Network (DNN) based real-time decoding...
  • COVID-CAPS

  • Referenced in 4 articles [sw41861]
  • surge of interest to develop Deep Neural Network (DNN)-based diagnosis solutions, mainly based...
  • OpenSALICON

  • Referenced in 2 articles [sw25869]
  • with an architecture based on Deep Neural Network (DNN). It leverages the representational power...
  • SqueezeNet

  • Referenced in 21 articles [sw30749]
  • model size. Recent research on deep neural networks has focused primarily on improving accuracy ... typically possible to identify multiple DNN architectures that achieve that accuracy level. With equivalent accuracy...
  • WebDNN

  • Referenced in 1 article [sw27147]
  • execution framework on web browser. Deep neural network (DNN) is getting much attention...
  • Februus

  • Referenced in 1 article [sw41289]
  • insidious Trojan attacks on Deep Neural Network (DNN) systems at run-time. In Trojan attacks ... activates a backdoor crafted in a deep neural network model using a secret trigger...
  • AUTOTRAINER

  • Referenced in 1 article [sw41850]
  • With machine learning models especially Deep Neural Network (DNN) models becoming an integral part...
  • DANN

  • Referenced in 1 article [sw31340]
  • CADD to train a deep neural network (DNN). DNNs can capture non-linear relationships among ... Device Architecture-compatible graphics processing units and deep learning techniques such as dropout and momentum...
  • Sim4CV

  • Referenced in 1 article [sw35045]
  • benchmark evaluation tool and a deep neural network (DNN) architecture for training vehicles to drive...
  • SparseNN

  • Referenced in 1 article [sw25870]
  • Input and Output Sparsity. Contemporary Deep Neural Network (DNN) contains millions of synaptic connections with...
  • SenHint

  • Referenced in 0 articles [sw36774]
  • modeling using a variety of deep neural networks (DNN). Unfortunately, their practical performance may fall ... SenHint, which integrates the output of deep neural networks and the implication of linguistic hints...
  • DeepTest

  • Referenced in 1 article [sw41849]
  • advances in Deep Neural Networks (DNNs) have led to the development of DNN-driven autonomous...
  • SyReNN

  • Referenced in 1 article [sw40543]
  • tool for analyzing deep neural networks. Deep Neural Networks (DNNs) are rapidly gaining popularity ... tool for understanding and analyzing a DNN by computing its extit{symbolic representation...
  • PhysNet

  • Referenced in 2 articles [sw40931]
  • efficiency and scalability to large datasets, deep neural networks (DNNs) are a particularly promising ... chemical applications. This work introduces PhysNet, a DNN architecture designed for predicting energies, forces...
  • DeepPINK

  • Referenced in 3 articles [sw42244]
  • made to facilitate the interpretability of deep neural networks (DNNs), existing methods are susceptible ... controlled error rate. By designing a new DNN architecture and integrating it with the recently ... maintaining high power. This new method, DeepPINK (Deep feature selection using Paired-Input Nonlinear Knockoffs...
  • NeST

  • Referenced in 2 articles [sw31716]
  • grow-and-prune paradigm. Deep neural networks (DNNs) have begun to have a pervasive impact ... However, the problem of finding an optimal DNN architecture for large applications is challenging. Common...
  • Privado

  • Referenced in 1 article [sw32567]
  • service?” We first demonstrate that DNN models executing inside enclaves are vulnerable to access pattern ... PRIVADO is input-oblivious: it transforms any deep learning framework that is written ... average on 11 different contemporary neural networks...
  • Rx-Caffe

  • Referenced in 1 article [sw25898]
  • Deep Neural Networks on Resistive Crossbars. Deep Neural Networks (DNNs) are widely used to perform ... compactly and efficiently realize the primitive DNN operation, viz., vector-matrix multiplication. However, in practice...
  • NATTACK

  • Referenced in 1 article [sw32886]
  • understanding how to construct robust deep neural networks (DNNs) and for thoroughly testing defense techniques ... benign input to a targeted DNN, our algorithm finds a probability density distribution over...