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

pLocmGneg
 Referenced in 23 articles
[sw25190]
 Gramnegative 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 deeplearning 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 nonEuclidean 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 Dockingbased Virtual Screening with Deep Learning. In this work, we propose a deep ... learning approach to improve dockingbased 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 encoderdecoder 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 highdimensional densities ... trivial, yet we maintain the ability to learn complex nonlinear 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 WeisfeilerLehman 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 physicsinformed deep learning using artificial neural networks. In this paper ... scientific computing and physicsinformed deep learning using artificial neural networks. SciANN uses the widely ... used deeplearning 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]
 highperformance deep learning inference. It includes a deep learning inference optimizer and runtime that ... latency and highthroughput 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...