YOLOv3: An Incremental Improvement

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

@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. …

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 …

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

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

state regulation brief - National Association of Insurance ...

The Purpose and Struc ture of Insurance Regulation The fundamental reason for government regulation of insurance is to protect American consumers. State systems are accessible and accountable to the public and sensitive to local social and economic conditions. State regulation has proven that it effectively protects

  Regulations, True, Crust, Struc tures

5 CONSTRAINT SATISFACTION PROBLEMS

5.3). Perhaps most importantly, the standard representation of the goal test reveals the struc-ture of the problem itself (Section 5.4). This leads to methods for problem decomposition and to an understanding of the intimate connection between the structure of a problem and the difficulty of solving it. 5.1 CONSTRAINT SATISFACTION PROBLEMS

  Satisfaction, Structure, True, Constraints, Crust, Struc tures, Constraint satisfaction

ISO 31000 - Risk management

be part of the organization’s struc-ture, processes, objectives, strategy and activities. It places a greater focus on creating value as the key driver of risk management and features other related principles such as con-tinual improvement, the inclusion of stakeholders, being customized to the organization and consideration of

  True, Crust, Struc tures

Logic models: A tool for effective program planning ...

A logic model is a visual representation of the assumptions and theory of action that underlie the struc - ture of an education program. A program can be a strategy for instruction in a classroom, a training session for a group of teachers, a grade-level curriculum, a building-level intervention, or a district- or statewide initiative.

  Model, True, Logic, Logic model, Crust, Struc tures

Ciphertext-Policy Attribute-Based Encryption

tonic “access tree”, where nodes of the access struc-ture are composed of threshold gates and the leaves describe attributes. We note that AND gates can be constructed as n-of-nthreshold gates and OR gates as 1-of-nthreshold gates. Furthermore, we can handle more complex access controls such as numeric ranges

  Policy, Based, True, Encryption, Attribute, Crust, Struc tures, Ciphertext policy attribute based encryption, Ciphertext

CELL — S TRUCTURE AND FUNCTIONS Y - NCERT

CELL — STRUCTURE AND FUNCTIONS 95 Cytoplasm It is the jelly-like substance present between the cell membrane and the nucleus. Various other components, or organelles, of cells are present in the

  Structure, Cells, Tructures, Struc tures

Small and Medium-sized Enterprises: Local Strength, Global ...

ture capital market is not only about financing, it is also about high-qual-ity management of the information and agency problems that arise when innovators, entrepreneurs and finan-ciers try to understand each other and co-operate in the launching of risky ventures. Fostering an ade-quate flow of financing for small firms is a crucial step in ...

  True