V
Vincent Vanhoucke
Researcher at Google
Publications - 84
Citations - 118049
Vincent Vanhoucke is an academic researcher from Google. The author has contributed to research in topics: Artificial neural network & Reinforcement learning. The author has an hindex of 42, co-authored 75 publications receiving 87969 citations. Previous affiliations of Vincent Vanhoucke include Stanford University.
Papers
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Proceedings Article
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
TL;DR: In this paper, the authors show that training with residual connections accelerates the training of Inception networks significantly, and they also present several new streamlined architectures for both residual and non-residual Inception Networks.
Proceedings Article
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
TL;DR: In this article, the authors show that training with residual connections accelerates the training of Inception networks significantly, and they also present several new streamlined architectures for both residual and non-residual Inception Networks.
Posted Content
Going Deeper with Convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: A deep convolutional neural network architecture codenamed Inception is proposed that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
Journal Article
Deep Neural Networks for Acoustic Modeling in Speech Recognition
Geoffrey E. Hinton,Li Deng,Dong Yu,George E. Dahl,Abdelrahman Mohamed,Navdeep Jaitly,Andrew W. Senior,Vincent Vanhoucke,Patrick Nguyen,Tara N. Sainath,Brian Kingsbury +10 more
TL;DR: This paper provides an overview of this progress and repres nts the shared views of four research groups who have had recent successes in using deep neural networks for a coustic modeling in speech recognition.
Proceedings Article
QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
Dmitry Kalashnikov,Alex Irpan,Peter Pastor,Julian Ibarz,Alexander Herzog,Eric Jang,Deirdre Quillen,Ethan Holly,Mrinal Kalakrishnan,Vincent Vanhoucke,Sergey Levine +10 more
TL;DR: QT-Opt as mentioned in this paper is a scalable self-supervised vision-based reinforcement learning framework that can leverage over 580k real-world grasp attempts to train a deep neural network Q-function with over 1.2M parameters.