MagFace: A Universal Representation for Face Recognition ...

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

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

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

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

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

Deep High-Resolution Representation Learning for Human ...

Deep High-Resolution Representation Learning for Human Pose Estimation Ke Sun1,2∗† Bin Xiao2∗ Dong Liu1 Jingdong Wang2‡ 1University of Science and Technology of China 2Microsoft Research Asia sunk@mail.ustc.edu.cn, dongleiu@ustc.edu.cn, {Bin.Xiao,jingdw}@microsoft.com Abstract In this paper, we are interested in the human pose es-

  High, Human, Learning, Representation, Resolution, High resolution representation learning for human

Unsupervised Visual Representation Learning by Context ...

high-resolution natural images. Unsupervisedrepresentation learning can also be formu-lated as learning an embedding (i.e. a feature vector for each image) where images that are semantically similar are close, while semantically different ones are far apart. One way to build such a representation is to create a supervised

  High, Learning, Visual, Representation, Resolution, Unsupervised, Unsupervised visual representation learning by

Deep Bilateral Learning for Real-Time Image Enhancement

an intermediate representation, a local affine color transformation that will be applied to the input through a new multiplicative node. 3) While most of our learning and inference is performed at low resolution, the loss function used during training is evaluated at full resolution, which causes the low-resolution transformations we

  Learning, Representation, Resolution

Abstract

learning: modeling high-level representations from raw observations remains elusive. Further, it is not always clear what the ideal representation is and if it is possible that one can learn such a representation without additional supervision or specialization to a particular data modality.

  High, Learning, Representation

Introduction - Deep Learning

human time and e ffort; it can take decades for an entire community of researchers. The quintessential example of a representation learning algorithm is the au-toencoder. An autoencoder is the combination of an encoder function that converts the input data into a different representation, and a decoder function

  Human, Learning, Representation, Representation learning

Dual Super-Resolution Learning for Semantic Segmentation

Dual Super-Resolution Learning for Semantic Segmentation Li Wang1, ∗, Dong Li1, Yousong Zhu2, Lu Tian1, Yi Shan1 1 Xilinx Inc., Beijing, China. 2 Institute of Automation, Chinese Academy of Sciences, Beijing, China. {liwa, dongl, lutian, yishan}@xilinx.com, yousong.zhu@nlpr.ia.ac.cn Abstract Current state-of-the-art semantic segmentation method-

  Learning, Resolution, Resolution learning

Knowledge Representation and Reasoning

– Knowledge Representation & Reasoning by Brachman & Levesque (available online) • Lectures – Tuesday and Thursday, 12:50-2:05, 300-300 • Grades – Four Assignments (40%), Mid-term (25%), Final (35%) • Prerequisites – First order logic and Resolution (at the level of CS157) • There will be two tutorial sections to cover this material

  Representation, Resolution

Natural Language Processing - Tutorialspoint

learning algorithms for language processing. Study of Human Languages Language is a crucial component for human lives and also the most fundamental aspect of our behavior. We can experience it in mainly two forms – written and spoken. In the written form, it is a way to pass our knowledge from one generation to the next. In the

  Human, Learning, Tutorialspoint, For human