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

Learning Deep Features for Discriminative Localization

weights of the output layer on to the convolutional feature maps, a technique we call class activation mapping. As illustrated in Fig. 2, global average pooling outputs the spatial average of the feature map of each unit at the last convolutional layer. A weighted sum of these values is used to generate the final output. Similarly, we compute a

  Feature, Deep, Activation, Convolutional, Convolutional features

Image Style Transfer Using Convolutional Neural Networks

cent advance of Deep Convolutional Neural Networks [18] ... tent in generic feature representations that generalise across datasets [6] and even to other visual information processing tasks [19, 4, 2, 9, 23], including texture recognition [5] and ... ij is the activation of the ith filter at position j in layer l.

  Feature, Styles, Generic, Transfer, Deep, Activation, Convolutional, Deep convolutional, Style transfer, Generic feature

Siamese Neural Networks for One-shot Image Recognition

between the twin feature vectors h 1 and h 2 combined with a sigmoid activation, which maps onto the interval [0;1]. Thus a cross-entropy objective is a natural choice for train-ing the network. Note that in LeCun et al., they directly learned the similarity metric, which was implictly defined by the energy loss, whereas we fix the metric as ...

  Feature, Activation

SuperPoint: Self-Supervised Interest Point Detection and ...

learning problem, and the Scale-Invariant Feature Trans-form, or SIFT [15], is still probably the most well-known traditional local feature descriptor in computer vision. Our SuperPoint architecture is inspired by recent ad-vances in applying deep learning to interest point detection and descriptor learning. At the ability to match image sub-

  Feature, Deep

Squeeze-and-Excitation Networks

Squeeze-and-Excitation Networks Jie Hu1∗ Li Shen2∗ Gang Sun1 hujie@momenta.ai lishen@robots.ox.ac.uk sungang@momenta.ai 1 Momenta 2 Department of Engineering Science, University of Oxford Abstract Convolutional neural networks are built upon the con-

  Convolutional

DeCAF: A Deep Convolutional Activation Feature for …

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition (a) LLC (b) GIST (c) DeCAF 1 (d) DeCAF 6 Figure 1. This figure shows several t-SNE feature visualizations on the ILSVRC-2012 validation set. (a) LLC , (b) GIST, and features derived from our CNN: (c) DeCAF 1, the first pooling layer, and (d) DeCAF

  Feature, Generic, Deep, Activation, Convolutional, A deep convolutional activation feature for, A deep convolutional activation feature for generic

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

  Deep, Convolutional, Deep convolutional

Deep Image Prior - CVF Open Access

mation contained within the activations of deep neural net-works. For this, we consider the “natural pre-image” tech-nique of [21], whose goal is to characterize the invariants learned by a deep network by inverting it on the set of nat-ural images. …

  Deep, A deep

Delving Deep into Rectifiers: Surpassing Human-Level ...

method for deep rectifier networks (Sec. 2.2). 2.1. Parametric Rectifiers We show that replacing the parameter-free ReLU by a learned activation unit improves classification accuracy2. Definition. Formally, we define an activation function: f(yi)= yi, if yi > 0 aiyi, if yi ≤ 0. (1) Here yi is the input of the nonlinear activation f on ...

  Deep, Activation