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

MAX3160/MAX3161/MAX3162 +3.0V to+5.5V, 1µA, RS-232/ …

to allow RS-232 and RS-485/422 compliant perfor-mance from a +3V to +5.5V supply. These devices also feature pin-selectable transmitter slew rates for both RS-232 and RS-485/422 modes. Slew-rate limiting mini-mizes EMI and reduces reflections caused by improp-erly terminated cables, allowing error-free data transmission up to 250kbps.

  Perfor

Incorporating Nesterov Momentum into Adam

To test this algorithm, we compared the perfor-mance of nine algorithms–GD, Momentum, NAG, RMSProp, Adam, Nadam, MaxaProp, AdaMax, and Nadamax–on three benchmarks–word2vec [10], MNIST image classification [7], and LSTM lan-guage models [17]. All algorithms used n = :999 and e = 1e 8 as suggested in [5], with a momen-tum schedule given by m

  Perfor, Nesterov

Returns to Buying Winners and Selling Losers: Implications ...

The longer term perfor- mance of the stocks included in the relative strength portfolios is examined in Section VI and Section VII back tests the strategy over the 1927 to 1964 3~orinstance, one of the inputs used by Value Line to assign a timeliness rank for each stock

  Perfor

Conditional Image Synthesis with Auxiliary Classifier GANs

to perform additional tasks is known to improve perfor-mance on the original task (e.g. (Sutskever et al.,2014; Szegedy et al.,2014;Ramsundar et al.,2016)). In addi-tion, an auxiliary decoder could leverage pre-trained dis-criminators (e.g. image classifiers) for further improving the synthesized images (Nguyen et al.,2016). Motivated

  Perfor, Conditional

Rainbow: Combining Improvements in Deep Reinforcement …

benchmark, both in terms of data efficiency and final perfor-mance. We also provide results from a detailed ablation study that shows the contribution of each component to overall per-formance. Introduction The many recent successes in scaling reinforcement learn-ing (RL) to complex sequential decision-making problems

  Rainbow, Perfor

The Abstraction: The Process

2 THE ABSTRACTION: THE PROCESS TIP: USE TIME SHARING (AND SPACE SHARING) Time sharing is a basic technique used by an OS to share a resource. By allowing the resource to be used for a little while by one entity, and then a little while by another, and so forth, the resource in question (e.g., the

  Process, The process, Abstraction, The abstraction

LTC4054-4.2/LTC4054X-4.2 - Standalone Linear Li-Ion ...

TYPICAL PERFOR A CE CHARACTERISTICS UW PROG Pin Voltage vs Supply Voltage(Constant Current Mode) PROG Pin Voltage vs Temperature Charge Current vs PROG Pin Voltage V PROG (V) VCC (V) 4.0 V PROG (V) 1.015 1.010 1.005 1.000 0.995 0.990 0.985 4.5 5.0 5.5 6.0 4054 G01 6.5 7.0 TEMPERATURE (°C) –50 –25 0 5025 75 100 4054 G02 1.0100 …

  Perfor

Configuring IPsec VPN Fragmentation and MTU

Note If the supervisor engine perfor ms GRE encapsulation, the encapsul ated packets will have the DF bit set. For general information on fragmenta tion and MTU issues, see “Resolve IP Fragmentation, MTU, MSS, and PMTUD Issues with GRE and IPsec” at this URL:

  Perfor

Samsung Magician 7

Perfor mance Bench mark RAPID Perfor mance Optimi zation (TRIM) Diagno stic Scan PSID Revert Encrypte d Drive Over provisio ning Secure Erase Security Setting 840 PRO √ √ √ 840 √ √ √ √ √ √ 840 EVO √ √ √ √ √ √ √ √ 750 EVO √ √ 850 PRO √ √ √ √ √ √ √ √

  Perfor