Understanding deep learning requires rethinking …

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

A Lecture on Model Predictive Control

optimization problem ... stochastic model) • on-line estimation of parameters / states • “robust” solution of optimization. FCCU Debutanizer ~20% under capacity. Debutanizer Diagram Reflux Fan Slurry Pump Around PCT RVP Pressure Flooding Tray 20 Temp. Feed Pre-Heater 160 F 400 F 190 lb From Stripper

  Optimization, Stochastic

2007 NIPS Tutorial on: Deep Belief Nets

stochastic variables. •We get to observe some of the variables and we would like to solve two problems: •The inference problem: Infer the states of the unobserved variables. •The learning problem: Adjust the interactions between variables to make the network more likely to generate the observed data. stochastic hidden cause visible effect

  2007, Tutorials, Deep, Inps, Nets, Belief, Stochastic, 2007 nips tutorial on, Deep belief nets

Model Predictive Control - Stanford University

• MPC problem is highly structured (see Convex Optimization, §10.3.4) – Hessian is block diagonal – equality constraint matrix is block banded • use block elimination to compute Newton step – Schur complement is block tridiagonal with n×n blocks • can solve in order T(n+m)3 flops using an interior point method

  Control, Predictive, Optimization, Predictive control

algorithms - arxiv.org

2.2 Stochastic gradient descent Stochastic gradient descent (SGD) in contrast performs a parameter update for each training example x(i) and label y(i): = r J( ;x(i);y(i)) (2) Batch gradient descent performs redundant computations for large datasets, as it recomputes gradients for similar examples before each parameter update.

  Stochastic

Taking the Human Out of the Loop: A Review of Bayesian ...

improvements. Bayesian optimization is a powerful tool for the joint optimization of design choices that is gaining great popularity in recent years. It promises greater automation so as to increase both product quality and human productivity. This review paper introduces Bayesian optimization, highlights some

  Optimization, Bayesian, Bayesian optimization

Objectives and Constraints for Wind Turbine Optimization

Objectives and Constraints for WindTurbine Optimization S.Andrew Ning National Renewable Energy Laboratory January, 2013 NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by …

  Optimization, Nrel