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

5: Introduction to Estimation - San Jose State University

Statistical inference . Statistical inference is the act of generalizing from the data (“sample”) to a larger phenomenon (“population”) with calculated degree of certainty. The act of generalizing and deriving statistical judgments is the process of inference. [Note: There is a distinction between causal inference and statistical inference.

  States, University, Jose, Inference, Casual, San jose state university, Causal inference

mediation: R Package for Causal Mediation Analysis

2008). In recent years, however, causal mechanisms have been studied within the modern framework of causal inference with an emphasis on the assumptions required for identi - cation. This approach has highlighted limitations of earlier methods and pointed the way towards a more exible estimation strategy. In addition, new research designs have been

  Analysis, Inference, Packages, Mediation, Casual, Package for causal mediation analysis, Causal inference

A review of mediation analysis in Stata: principles ...

Causal inference framework Let A be atreatment, M be amediator, Y be anoutcome, Let Y(a) be the potential outcome Y when intervening to set A to a Let M(a) be the potential outcome M when intervening to set A to a Let Y(a;m) be the potential outcome Y when intervening to set A to

  Analysis, Principles, Inference, Stata, Mediation, Casual, Causal inference, Mediation analysis in stata

Causal Inference in Machine Learning

Causal models, revisited Instead of an exhaustive “table of interventional distributions”: G = (V, E), a causal graph with vertices V and edges E P( ), a probability over the “natural state” of V, parameterized by (G, ) is a causal model if pair (G, P) satisfies the Causal Markov condition

  Inference, Casual, Causal inference