InfoGAN: Interpretable Representation Learning by ...

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

Understanding Contrastive Representation Learning through ...

representation learning in fact directly optimizes for these two properties in the limit of infinite negative samples. We propose theoretically-motivated metrics for alignment and uniformity, and observe strong agreement between them and downstream …

  Learning, Representation, Representation learning

A Simple Framework for Contrastive Learning of Visual ...

Representation learning with contrastive cross entropy loss benefits from normalized embeddings and an appro-priately adjusted temperature parameter. Contrastive learning benefits from larger batch sizes and longer training compared to its supervised counterpart. Like supervised learning, contrastive learning benefits from deeper and wider ...

  Learning, Representation, Representation learning

The Role of Visual Learning in Improving Students’ High ...

The visual representation of algorithms is useful both for teachers and pupils in their teaching and learning. Problem-based learning (PBL) leads to the development of higher-order thinking (HOT) skills and

  High, Students, Learning, Improving, Visual, Representation, Visual learning in improving students high

DeepSDF: Learning Continuous Signed Distance Functions for ...

DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation Jeong Joon Park1 , 3Peter Florence 2 Julian Straub Richard Newcombe Steven Lovegrove3 1University of Washington 2Massachusetts Institute of Technology 3Facebook Reality Labs Figure 1: DeepSDF represents signed distance functions (SDFs) of shapes via latent code-conditioned …

  Learning, Representation

Momentum Contrast for Unsupervised Visual Representation ...

vised visual representation learning. From a perspective on contrastive learning [29] as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dic-tionary on-the-fly that facilitates contrastive unsupervised learning. MoCo provides competitive results under the

  Learning, Representation, Representation learning

InfoGAN: Interpretable Representation Learning by ...

representation learning [1,2], whose goal is to use unlabelled data to learn a representation that exposes important semantic features as easily decodable factors. A method that can learn such representations is likely to exist [2], and to be useful for …

  Learning, Representation, Representation learning

metapath2vec: Scalable Representation Learning for ...

representation learning methods enable the automatic discovery of useful and meaningful (latent) features from the “raw networks.” However, these work has thus far focused on representation learning for homogeneous networks—representative of singular type of nodes and relationships. Yet a large number of social and

  Learning, Representation, Representation learning