Sequence to Sequence Learning with Neural Networks

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

Spatio-Temporal Graph Convolutional Networks: A Deep ...

Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for TrafÞc Forecasting Bing Yu! 1, Haoteng Yin! 2,3, Zhanxing Zhu 3,4 1 School of Mathematical Sciences, Peking University, Beijing, China 2 Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China 3 Center for Data Science, Peking University, Beijing, China

  Network, Deep, Graph, Convolutional, Temporal, Positas, Spatio temporal graph convolutional networks, A deep

Learning Transferable Features with Deep Adaptation Networks

deep networks, resulting in statistically unboundedrisk for target tasks (Mansour et al., 2009; Ben-David et al., 2010). Our work is primarily motivated by Yosinski et al. (2014), which comprehensively explores feature transferability of deep convolutional neural networks. The method focuses on a different scenario where the learning tasks are ...

  Network, Deep, Deep networks

“Deep Fakes” using Generative Adversarial Networks (GAN)

two GAN networks, and other than the loss in the tradi-tional GAN network, it also included a cycle-consistency loss to ensure any input is mapped to a relatively reasonable output. 2. Physical and Mathematical framework The framework we used in this project is a Cycle-GAN based on deep convolutional GANs. 2.1. Generative Adversarial Networks (GAN)

  Network, Using, Deep, Efka, Adversarial, Generative, Deep fakes using generative adversarial networks

Understanding the difficulty of training deep feedforward ...

deep networks with sigmoids but initialized from unsuper-vised pre-training (e.g. from RBMs) do not suffer from this saturation behavior. Our proposed explanation rests on the hypothesis that the transformation that the lower layers of the randomly initialized network computes initially is

  Network, Deep, Deep networks

Multifaceted Feature Visualization: Uncovering the ...

We can better understand deep neural networks by identifying which features each of their neu-rons have learned to detect. To do so, researchers have created Deep Visualization techniques in-cluding activation maximization, which synthet-ically generates inputs (e.g. images) that maxi-mally activate each neuron. A limitation of cur-

  Network, Deep