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

CNNs for Face Detection and Recognition

deep learning approach using neural network has achieved significant success in tackling face detection as a subclass of object classification, localization, and detection. Appar-ently, the evolve of face detection correlates closely with the development of object classification, localization and detec-tion techniques. 2.1. Sliding Window

  Using, Into, Faces, Recognition, Object, Detection, Detec, Localization, Detec tion, For face detection and recognition

Multi-scale Patch Aggregation (MPA) for Simultaneous ...

Object Detection Object detection has a long history in computer vision. Before DCNN shows its great abil-ity for image classification [21, 33], part-based models [9, 37] were popular. Recent object detection framework-s [11, 12, 17, 37, 29, 34, 23, 32, 10] are based on DCNN [21, 33] to classify object proposals. These methods either

  Patch, Aggregation, Object, Detection, Simultaneous, Object detection, Patch aggregation, For simultaneous, Object detection object detection

1 Object Detection in 20 Years: A Survey - arXiv

1 Object Detection in 20 Years: A Survey Zhengxia Zou, Zhenwei Shi, Member, IEEE, Yuhong Guo, and Jieping Ye, Senior Member, IEEE Abstract—Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in …

  Object, Detection, Object detection

Object Recognition from Local Scale-Invariant Features

Object recognition in cluttered real-world scenes requires local image features that are unaffected by nearby clutter or partial occlusion. The features must be at least partially in-variant to illumination,3D projective transforms, and com …

  Local, Object

AAAI-21 Accepted Paper List.1.29

! 4!! 228:!Rain!Streak!Removal!via!Dual!Graph!Convolutional!Network! Xueyang!Fu,!Qi!Qi,!Yurui!Zhu,!Xinghao!Ding,!Zheng*Jun!Zha!! 233:!RevMan:!Revenue*Aware!Multi*Task ...