Abstract

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

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

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. …

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

“L” Matching Networks - UC Santa Barbara

“L” Matching Networks 8 possibilities for single frequency (narrow-band) lumped element matching networks. Figure is from: G. Gonzalez, Microwave Transistor Amplifiers: Analysis and Design, Second Ed., Prentice Hall, 1997. These networks are used to cancel the reactive component of the load and transform the

  Network, Matching, Matching networks

Chapter 5 – Impedance Matching and Tuning - ITTC

3/12/2007 Matching Networks and Transmission Lines 1/7 Jim Stiles The Univ. of Kansas Dept. of EECS Matching Networks and Transmission Lines Recall that a primary purpose of a transmission line is to allow the transfer of power from a source to a load. Q: So, say we directly connect an arbitrary source to an

  Network, Matching, Matching networks

Impedance Matching - QSL.net

• The matching process becomes more difficult when real parts of the terminations are unequal, or when they have complex impedances. Example: Match a 50 Ω resistive source at 100MHz, to a 50 Ω resistive load that has in series a 1.59pF ... Impedance Matching using Resistor Networks

  Network, Matching

Superpixel Segmentation With Fully Convolutional …

In the past few years, deep networks [45, 31, 29, 44] taking advantage of large-scale annotated data have gen-erated impressive stereo matching results. Recent meth-ods [17, 7, 8] employing 3D convolution achieve the state-of-the-art performance on public benchmarks. However, due to the memory constraints, these methods typically

  Network, With, Matching, Fully, Segmentation, Superpixels, Convolutional, Superpixel segmentation with fully convolutional

Matching Networks - University of California, Berkeley

The L-matching networks are designed as follows: 1 Calculate the boosting factor m = R hi R lo. 2 Compute the required circuit Q by (1 + Q2) = m, or Q = p m 1. 3 Pick the required reactance from the Q. If you’re boosting the resistance, e.g. R S > R L, then X s = Q R L. If you’re

  Network, Matching, Matching networks

Matching Networks for One Shot Learning - NeurIPS

Figure 1: Matching Networks architecture showing only a few examples per class, switching the task from minibatch to minibatch, much like how it will be tested when presented with a few examples of a new task. Besides our contributions in defining a model and training criterion amenable for one-shot learning,

  Network, Matching, Matching networks

Algorithms for Graph Similarity and Subgraph Matching

matching, which are both important practical problems useful in several fields of science, engineer-ing and data analysis. For the problem of graph similarity, we develop and test a new framework for solving the problem using belief propagation and …

  Matching, Algorithm, Graph, Similarity, Algorithms for graph similarity and subgraph matching, Subgraph

ABSTRACT arXiv:1409.1556v6 [cs.CV] 10 Apr 2015

arXiv:1409.1556v6 [cs.CV] 10 Apr 2015 Published as a conference paper at ICLR 2015 VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION Karen Simonyan∗ & Andrew Zisserman+ Visual Geometry Group, Department of Engineering Science, University of Oxford

  Network