On the black hole/string transition

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

Lecture Notes on General Relativity - Portal

Lecture Notes on General Relativity MatthiasBlau Albert Einstein Center for Fundamental Physics Institut fu¨r Theoretische Physik Universit¨at Bern

The Quantum Hall E ect - University of Cambridge

2.2.2 The Role of Gauge Invariance 51 2.2.3 An Aside: The Kubo Formula 54 2.2.4 The Role of Topology 57 2.3 Particles on a Lattice 61 2.3.1 TKNN Invariants 61 2.3.2 The Chern Insulator 65 2.3.3 Particles on a Lattice in a Magnetic Field 67 { 1

  Quantum, Hall, Invariance, The quantum hall e ect

DIFFERENTIAL GEOMETRY - ELTE

Balazs Csik os DIFFERENTIAL GEOMETRY E otv os Lor and University Faculty of Science Typotex 2014

Conformal Field Theory - Nikhef

1.1.3 Conformal invariance A conformal transformation can now be de ned as a coordinate transformation which acts on the metric as a Weyl transformation. Consider a general coordinate transformation x!x0, such that x = f (x0 ). This has the following e ect on the metric g (x) !g0 (x 0) = @fˆ @x 0 @f˙ @x g ˆ˙(f(x0)) : (1.8)

  Conformal, Invariance, Conformal invariance