Deep Reinforcement Learning with Double Q-learning

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

Learning Deep Features for Discriminative Localization

combine multiple-instance learning with CNN features to localize objects. Oquab et al [15] propose a method for transferring mid-level image representations and show that some object localization can be achieved by evaluating the output of CNNs on multiple overlapping patches. However, the authors do not actually evaluate the localization ability.

  Learning

Psychological Safety and Learning Behavior in Work Teams

ratory groups, has not investigated the learning processes of real work teams (cf. Argote, Gruenfeld, and Naquin, 1999). Although most studies of organizational learning have been field-based, empirical research on group learning has primar- ily taken place in the laboratory, and little research has been

  Learning

Understanding deep learning requires rethinking …

stability of a learning algorithm is independent of the labeling of the training data. Hence, the concept is not strong enough to distinguish between the models trained on the true labels (small generalization error) and models trained on random labels (high generalization error). This also

  Understanding, Learning, Deep, Errors, Generalization, Understanding deep learning, Generalization error

Random Forests - Springer

favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth Interna- tional conference , ∗∗∗, 148–156), but are more robust with …

  Learning

Contrastive Analysis and Error Analysis

and learning. What is Contrastive Analysis? CA is a systematic comparison between the target language and the students’ native language to know the similarities and differences between the two. The similarities are assumed to facilitate the learning of the target language, while the differences are predicted to cause learning problems.

  Learning, Errors

Contrastive Analysis, Error Analysis, Interlanguage 1

the two languages and cultures are similar, learning difficulties will not be expected, where they are different, then learning difficulties are to be expected, and the greater the difference, the greater the degree of expected difficulty. On the basis of such analysis, it was believed, teaching materials could be tailored to

  Analysis, Learning, Errors, Error analysis, Contrastive, Interlanguage, Contrastive analysis

On Calibration of Modern Neural Networks

work learning, but also provide a simple and straightforward recipe for practical settings: on most datasets, temperature scaling – a single-parameter variant of Platt Scaling – is surpris-ingly effective at calibrating predictions. 1. Introduction Recent advances in deep learning have dramatically im-

  Calibration, Learning

The Learning with Errors Problem

The Learning with Errors Problem Oded Regev Abstract In this survey we describe the Learning with Errors (LWE) problem, discuss its properties, its hardness, and its cryptographic applications. 1 Introduction In recent years, the Learning with Errors (LWE) problem, introduced in [Reg05], has turned out to

  With, Learning, Errors, Learning with errors

On Lattices, Learning with Errors, Random Linear Codes ...

On Lattices, Learning with Errors, Random Linear Codes, and Cryptography Oded Regev ⁄ May 2, 2009 Abstract Our main result is a reduction from worst-case lattice problems such as GAPSVP and SIVP to a certain learning problem. This learning problem is a natural extension of the ‘learning from parity with error’ problem to higher moduli.

  With, Learning, Errors, Learning with errors

Contrastive Analysis And Error Analysis

animal learning not human learning. 2) In the learning of a second language, the native language of the student does not really" interfere" with his learning, but it plays as an " escape hatch" when the learner gets into trouble. 3This view point suggests that what will be most difficult for the learner is his

  Learning, Errors