Non-local 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

Identity Mappings in Deep Residual Networks arXiv:1603 ...

Abstract Deep residual networks [1] have emerged as a family of ex-tremely deep architectures showing compelling accuracy and nice con-vergence behaviors. In this paper, we analyze the propagation formu-lations behind the residual building blocks, which suggest that the for-ward and backward signals can be directly propagated from one block

  Network, Building

Deformable Convolutional Networks

Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in their building modules. In this work, we introduce two new modules to enhance the transformation modeling capability of CNNs, namely, de-formable convolution and deformable RoI pooling. Both

  Network, Building

Neural Networks for Time Series Prediction

15-486/782: Artificial Neural Networks Fall 2006 (based on earlier slides by Dave Touretzky and Kornel Laskowski) What is a Time Series? A sequence of vectors (or scalars) which depend on time t. In this ... • air temperature in a building These phenomena may be discrete or continuous. 3. Discrete Phenomena

  Network, Building

Large-scale Video Classification with Convolutional Neural ...

cently, Convolutional Neural Networks (CNNs) [15] have been demonstrated as an effective class of models for un-derstanding image content, giving state-of-the-art results on image recognition, segmentation, detection and retrieval [11,3,2,20,9,18]. The key enabling factors behind these results were techniques for scaling up the networks to tens

  Network, Large, Scale, Classification, Video, Neural, Convolutional, Convolutional neural networks, Large scale video classification

Building a Communications Strategy Tactics, Tools and ...

Module 8: Building a Communications Strategy Developing a Strategic Communications Plan A strategic communications plan, or media strategy, is your plan for getting positive coverage of your campaign through the media that your target voters use the most, in order to communicate your message to these voters. Political campaigns benefit

  Building

BUDGET 2022–2023 Building on Success

our transportation networks. It also increases the demand for services that enhance our quality of life. ... transitions its spending plans from addressing COVID-19 pressures to building capacity and resiliency into government services. Mr. Speaker, as part of Budget 2021–2022, I was proud to release a gender ...

  Network, Building