Neural Discrete Representation 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

Lecture 13: Generative Models

Lecture 13 - 33 May 18, 2017 PixelRNN and PixelCNN Improving PixelCNN performance - Gated convolutional layers - Short-cut connections - Discretized logistic loss - Multi-scale - Training tricks - Etc… See - Van der Oord et al. NIPS 2016 - Salimans et al. 2017 (PixelCNN++) Pros: - Can explicitly compute likelihood p(x) - Explicit likelihood ...

  2017, Inps, Generative

Lecture 14: Reinforcement Learning

Lecture 14 - May 23, 2017 Case Study: Playing Atari Games 42 Objective: Complete the game with the highest score State: Raw pixel inputs of the game state Action: Game controls e.g. Left, Right, Up, Down Reward: Score increase/decrease at each time step [Mnih et al. NIPS Workshop 2013; Nature 2015]

  2017, Learning, Reinforcement, Inps, Reinforcement learning

CatBoost: gradient boosting with categorical features support

CatBoost: gradient boosting with categorical features support Anna Veronika Dorogush, Vasily Ershov, Andrey Gulin Yandex Abstract In this paper we present CatBoost, a new open-sourced gradient boosting library

  Catboost

Sentence-BERT: Sentence Embeddings using Siamese BERT …

2017). RoBERTa (Liu et al.,2019) showed, that the performance of BERT can further improved by small adaptations to the pre-training process. We also tested XLNet (Yang et al.,2019), but it led in general to worse results than BERT. A large disadvantage of the BERT network structure is that no independent sentence embed-

  2017

2017 NIPS Poster for web

LONG BEACH CA | DEC 4 - 9 | NIPS.CC NIPS 2017 TUTORIALS - DEC 4TH Statistical Relational Artificial Intelligence: Logic, Probability and Computation Luc De Raedt, David Poole, Kristian Kersting, Sriraam Natarajan Reinforcement Learning with People Emma Brunskill A Primer on Optimal Transport Marco Cuturi, Justin Solomon

  2017, Inps, 2017 nips, Nips 2017

Attention is All you Need - NIPS

31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. Recurrent models typically factor computation along the symbol positions of the input and output sequences. Aligning the positions to steps in computation time, they generate a …

  Information, System, 2017, Processing, Inps, Neural, Neural information processing systems, Nips 2017

A Unified Approach to Interpreting Model Predictions - NIPS

31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. 2. We then show that game theory results guaranteeing a unique solution apply to the entire class of additive feature attribution methods (Section 3) and propose SHAP values as …

  Information, System, 2017, Processing, Inps, Neural, Neural information processing systems, Nips 2017