O
Oriol Vinyals
Researcher at Google
Publications - 218
Citations - 121048
Oriol Vinyals 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 84, co-authored 200 publications receiving 82365 citations. Previous affiliations of Oriol Vinyals include University of California, San Diego & University of California, Berkeley.
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Proceedings ArticleDOI
Beyond short snippets: Deep networks for video classification
Joe Yue-Hei Ng,Matthew Hausknecht,Sudheendra Vijayanarasimhan,Oriol Vinyals,Rajat Monga,George Toderici +5 more
TL;DR: In this article, a recurrent neural network that uses Long Short-Term Memory (LSTM) cells which are connected to the output of the underlying CNN was proposed to model the video as an ordered sequence of frames.
Proceedings Article
Neural Discrete Representation Learning
TL;DR: The Vector Quantised-Variational AutoEncoder (VQ-VAE) as discussed by the authors is a generative model that learns a discrete latent representation by using vector quantization.
Proceedings Article
Pointer networks
TL;DR: A new neural architecture to learn the conditional probability of an output sequence with elements that are discrete tokens corresponding to positions in an input sequence using a recently proposed mechanism of neural attention, called Ptr-Nets, which improves over sequence-to-sequence with input attention, but also allows it to generalize to variable size output dictionaries.
Proceedings Article
Understanding deep learning requires rethinking generalization.
TL;DR: This article showed that deep neural networks can fit a random labeling of the training data, and that this phenomenon is qualitatively unaffected by explicit regularization, and occurs even if the true images are replaced by completely unstructured random noise.
Posted Content
A Neural Conversational Model
Oriol Vinyals,Quoc V. Le +1 more
TL;DR: A simple approach to conversational modeling which uses the recently proposed sequence to sequence framework, and is able to extract knowledge from both a domain specific dataset, and from a large, noisy, and general domain dataset of movie subtitles.