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

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

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: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

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. …

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

Collaborative Knowledge Base Embedding for Recommender …

network embedding method, termed as TransR, to extract items’ structural representations by considering the heterogeneity of both nodes and relationships. We apply stacked denoising auto-encoders and stacked convolutional auto-encoders, which are two types of deep learning based embedding techniques, to extract items’ tex-

  Network, Structural, Deep, Embedding, Network embedding

Structural Deep Network Embedding - SIGKDD

network structure well and are robust to sparse networks. In summary, the contributions of this paper are listed as follows: We propose a Structural Deep Network Embedding method, namely SDNE, to perform network embedding. The method is able to map the data to a highly non-linear latent space to preserve the network structure and is robust to ...

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

Deep Learning on Graphs - Michigan State University

4.2.2 Preserving Structural Role 86 4.2.3 Preserving Node Status 89 ... ing traditional graph embedding, modern graph embedding, and deep learn-ing on graphs. As the first generation of graph representation learning, tra- ... social network analysis, GNNs result in state-of-the-art performance and bring

  Network, Learning, Structural, Learn, Deep, Graph, Embedding, Deep learning on graphs, Deep learn ing on graphs

Heterogeneous Graph Neural Network

neural network architecture with two modules to aggregate feature information of those sampled neighboring nodes. The first module encodes “deep” feature interactions of heterogeneous contents and generates content embedding for each node. The second module aggregates content (attribute) embeddings of different neighboring

  Network, Deep, Embedding, Heterogeneous

Exploring Cross-Image Pixel Contrast for Semantic …

tures during segmentation network training [40,2,86]. Basically, these segmentation models (excluding [37]) utilize deep architectures to project image pixels into a highly non-linear embedding space (Fig.1(c)). However, they typically learn the embedding space that only makes use of “local” context around pixel samples (i.e., pixel de-

  Network, Deep, Embedding

Representation Learning on Graphs: Methods and Applications

encoding structural information about a graph (e.g., degree statistics or kernel functions). However, recent years have seen a surge in approaches that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction.

  Learning, Structural, Deep, Graph, Deep learning

DeepWalk: Online Learning of Social Representations

is used on Zachary’s Karate network [44] to generate a la-tent representation in R2. Note the correspondence between community structure in the input graph and the embedding. Vertex colors represent a modularity-based clustering of the input graph. 1. INTRODUCTION The sparsity of a network representation is both a strength and a weakness.

  Network, Embedding, Deepwalk

DeepWalk: Online Learning of Social Representations

network classi cation tasks for social networks such as Blog-Catalog, Flickr, and YouTube. Our results show that Deep-Walk outperforms challenging baselines which are allowed a global view of the network, especially in the presence of missing information. DeepWalk’s representations can pro-vide F 1 scores up to 10% higher than competing methods

  Network, Deep

arXiv:1706.02216v4 [cs.SI] 10 Sep 2018

inductive node embedding. Unlike embedding approaches that are based on matrix factorization, we leverage node features (e.g., text attributes, node profile information, node degrees) in order to learn an embedding function that generalizes to unseen nodes. By incorporating node features in the

  Embedding