arXiv:0706.3639v1 [cs.AI] 25 Jun 2007

arXiv:0706.3639v1 [cs.AI] 25 Jun 2007 Technical Report IDSIA-07-07 A Collection of Definitions of Intelligence Shane Legg IDSIA, Galleria …

  Intelligence, Collection

Deep Residual Learning for Image Recognition - …

Deep Residual Learning for Image Recognition Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun Microsoft Research fkahe, v-xiangz, v-shren, jiansung@microsoft.com

  Image, Learning, Residual, Recognition, Residual learning for image recognition

arXiv:1301.3781v3 [cs.CL] 7 Sep 2013

For all the following models, the training complexity is proportional to O = E T Q; (1) where E is number of the training epochs, T is the number of …

@google.com arXiv:1609.03499v2 [cs.SD] 19 Sep 2016

where 1 <x t <1 and = 255. This non-linear quantization produces a significantly better reconstruction than a simple linear quantization scheme. …

A Tutorial on UAVs for Wireless Networks: …

A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems Mohammad Mozaffari 1, ... to UAVs in wireless communications is the work in …

  Network, Communication, Wireless, Wireless communications, Wireless networks

Adversarial Generative Nets: Neural Network …

Adversarial Generative Nets: Neural Network Attacks on State-of-the-Art Face Recognition Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer Carnegie Mellon University

  Network, Attacks, Nets, Adversarial generative nets, Adversarial, Generative, Neural network, Neural, Neural network attacks

Massive Exploration of Neural Machine Translation ...

Massive Exploration of Neural Machine Translation Architectures Denny Britzy, Anna Goldie, Minh-Thang Luong, Quoc Le fdennybritz,agoldie,thangluong,qvlg@google.com Google Brain

  Architecture, Machine, Exploration, Translation, Neural, Exploration of neural machine translation, Exploration of neural machine translation architectures

Mastering Chess and Shogi by Self-Play with a …

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm David Silver, 1Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, 1Matthew Lai, Arthur Guez, Marc Lanctot,1

Going deeper with convolutions - arXiv

Going deeper with convolutions Christian Szegedy Google Inc. Wei Liu University of North Carolina, Chapel Hill Yangqing Jia Google Inc. Pierre Sermanet

  With, Going, Going deeper with convolutions, Deeper, Convolutions

Andrew G. Howard Menglong Zhu Bo Chen Dmitry ...

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand Marco Andreetto Hartwig Adam

  Applications

Structural Deep Network Embedding - SIGKDD

DeepWalk [21] combined random walk and skip-gram to learn network representations. Although empirically effective, it lacks a clear objective function to articulate how to preserve the network structure. It is prone to preserving only the second-order proximity. However, our method designs an explicit objective function, which

  Network, Structural, Deep, Embedding, Structural deep network embedding, Deepwalk

Representation Learning on Graphs: Methods and Applications

dimensional visualization of node embeddings generated from this graph using the DeepWalk method (Section 2.2.2) [46]. The distances between nodes in the embedding space reflect proximity in the original graph, and the node embeddings are spatially clustered according to the different color-coded communities. Reprinted with permission from [46 ...

  Learning, Graph, Deepwalk

Link Prediction Based on Graph Neural Networks

a number of network embedding techniques have been proposed, such as DeepWalk [19], LINE [21] and node2vec [20], which are also latent feature methods since they implicitly factorize some matrices too [22]. Explicit features are often available in the form of node attributes, describing all kinds of side information about individual nodes.

  Based, Network, Link, Prediction, Graph, Neural, Deepwalk, Link prediction based on graph neural networks

Heterogeneous Graph Neural Network

DeepWalk [20], were initially developed to feed a set of short ran-dom walks over the graph to the SkipGram model [19] so as to approximate the node co-occurrence probability in these walks and obtain node embeddings. Subsequently, semantic-aware ap-proaches, e.д., metapath2vec [4], were proposed to address node

  Heterogeneous, Deepwalk

CS224W: Machine Learning with Graphs Jure Leskovec, http ...

Methods for node embeddings: DeepWalk, Node2Vec Graph Neural Networks: GCN, GraphSAGE, GAT, Theory of GNNs Knowledge graphs and reasoning: TransE, BetaE Deep generative models for graphs: GraphRNN Applications to Biomedicine, Science, Industry

  Deepwalk

T.N.Kipf@uva.nl M.Welling@uva.nl arXiv:1611.07308v1 [stat ...

(SC) [5] and DeepWalk (DW) [6]. Both SC and DW provide node embeddings Z. We use Eq. 4 (left side) to calculate scores for elements of the reconstructed adjacency matrix. We omit recent variants of DW [7, 8] due to comparable performance. Both SC and DW do not support input features. For VGAE and GAE, we initialize weights as described in [9].

  Deepwalk

DeepWalk: Online Learning of Social Representations

information. DeepWalk’s representations can provide F 1 scores up to 10% higher than competing methods when la-beled data is sparse. In some experiments, DeepWalk’s representations are able to outperform all baseline methods while using 60% less training data. DeepWalk is also scalable. It is an online learning algo-

  Deepwalk