• Pyro

  • Referenced in 11 articles [sw27079]
  • modeling, unifying the best of modern deep learning and Bayesian modeling. It was designed with...
  • pLoc-mGneg

  • Referenced in 23 articles [sw25190]
  • Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC. Information...
  • Dopamine

  • Referenced in 6 articles [sw31151]
  • Research Framework for Deep Reinforcement Learning. Deep reinforcement learning (deep RL) research has grown significantly...
  • CNTK

  • Referenced in 10 articles [sw21056]
  • Toolkit (https://cntk.ai), is a unified deep-learning toolkit that describes neural networks...
  • DeepMath

  • Referenced in 8 articles [sw27551]
  • knowledge, this is the first time deep learning has been applied to theorem proving...
  • DeCAF

  • Referenced in 21 articles [sw17856]
  • conduct experimentation with deep representations across a range of visual concept learning paradigms...
  • OctNet

  • Referenced in 5 articles [sw36665]
  • OctNet: Learning Deep 3D Representations at High Resolutions. We present OctNet, a representation for deep ... learning with sparse 3D data. In contrast to existing models, our representation enables 3D convolutional ... networks which are both deep and high resolution. Towards this goal, we exploit the sparsity...
  • CayleyNets

  • Referenced in 4 articles [sw38090]
  • combination with resounding success of deep learning in various applications, has brought the interest ... generalizing deep learning models to non-Euclidean domains. In this paper, we introduce ... spectral domain convolutional architecture for deep learning on graphs. The core ingredient of our model...
  • LIBXSMM

  • Referenced in 7 articles [sw23238]
  • matrix multiplications as well as for deep learning primitives such as small convolutions targeting Intel...
  • Cityscapes

  • Referenced in 7 articles [sw36624]
  • datasets, especially in the context of deep learning. For semantic urban scene understanding, however...
  • Geometer's Sketchpad

  • Referenced in 226 articles [sw04858]
  • through college—a tangible, visual way to learn mathematics that increases their engagement, understanding ... functions—from linear to trigonometric—promoting deep understanding. Sketchpad is the optimal tool for interactive...
  • Deep_Learning

  • Referenced in 3 articles [sw32225]
  • Expressiveness of Deep Learning. Deep learning has had a profound impact on computer science ... theoretical evidence for the superiority of deep learning over shallow learning. This formalization of their...
  • DeepVS

  • Referenced in 4 articles [sw16458]
  • Boosting Docking-based Virtual Screening with Deep Learning. In this work, we propose a deep ... learning approach to improve docking-based virtual screening. The introduced deep neural network, DeepVS, uses...
  • Quicksilver

  • Referenced in 6 articles [sw38623]
  • Quicksilver: Fast predictive image registration – A deep learning approach. This paper introduces Quicksilver, a fast ... model based directly on image appearance. A deep encoder-decoder network is used ... four standard validation datasets, and can jointly learn an image similarity measure. Quicksilver is freely...
  • NICE

  • Referenced in 6 articles [sw29631]
  • Independent Components Estimation. We propose a deep learning framework for modeling complex high-dimensional densities ... trivial, yet we maintain the ability to learn complex non-linear transformations, via a composition ... simple building blocks, each based on a deep neural network. The training criterion is simply...
  • subgraph2vec

  • Referenced in 4 articles [sw36496]
  • graphs inspired by recent advancements in Deep Learning and Graph Kernels. These latent representations encode ... information obtained from neighbourhoods of nodes to learn their latent representations in an unsupervised fashion ... could be used for building a deep learning variant of Weisfeiler-Lehman graph kernel...
  • PTE

  • Referenced in 6 articles [sw37756]
  • effectiveness. However, comparing to sophisticated deep learning architectures such as convolutional neural networks, these methods...
  • SciANN

  • Referenced in 3 articles [sw38344]
  • scientific computations and physics-informed deep learning using artificial neural networks. In this paper ... scientific computing and physics-informed deep learning using artificial neural networks. SciANN uses the widely ... used deep-learning packages TensorFlow and Keras to build deep neural networks and optimization models ... batch optimization and model reuse for transfer learning. SciANN is designed to abstract neural network...
  • TensorRT

  • Referenced in 3 articles [sw36236]
  • high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that ... latency and high-throughput for deep learning inference applications...
  • h2o

  • Referenced in 5 articles [sw17104]
  • data that computes parallel distributed machine learning algorithms such as generalized linear models, gradient boosting ... machines, random forests, and neural networks (deep learning) within various cluster environments...