Oriol Vinyals mostly deals with Artificial intelligence, Machine learning, Artificial neural network, Deep learning and Natural language processing. His research ties Pattern recognition and Artificial intelligence together. His work in Machine learning addresses subjects such as Inference, which are connected to disciplines such as State and Information extraction.
Oriol Vinyals has included themes like Ground truth, Theoretical computer science and Message passing in his Artificial neural network study. His Deep learning research is multidisciplinary, incorporating elements of Contextual image classification, Regularization, One-shot learning and Generalization error. Oriol Vinyals combines subjects such as Beam search, Speech recognition, Rule-based machine translation and Closed captioning with his study of Machine translation.
His scientific interests lie mostly in Artificial intelligence, Artificial neural network, Machine learning, Speech recognition and Reinforcement learning. His Artificial intelligence research is multidisciplinary, relying on both Natural language processing and Pattern recognition. His Pattern recognition research incorporates themes from Feature and Autoregressive model.
His study on Stochastic gradient descent is often connected to Sample as part of broader study in Artificial neural network. His study in Machine learning is interdisciplinary in nature, drawing from both Inference, Contextual image classification, Meta learning, Adaptation and Range. Oriol Vinyals has researched Speech recognition in several fields, including Discriminative model and State.
The scientist’s investigation covers issues in Artificial intelligence, Artificial neural network, Theoretical computer science, Machine learning and Reinforcement learning. His Artificial intelligence study focuses on Deep learning in particular. His research in Deep learning intersects with topics in Object, Segmentation and Computer vision.
His work deals with themes such as Tree, Feature and Convolutional neural network, Pattern recognition, which intersect with Artificial neural network. In his research on the topic of Theoretical computer science, Supervised learning is strongly related with Message passing. His Machine learning research incorporates elements of Pixel and Record locking.
His primary areas of investigation include Artificial intelligence, Machine learning, Artificial neural network, Deep learning and Reinforcement learning. The Regularization and Data point research Oriol Vinyals does as part of his general Artificial intelligence study is frequently linked to other disciplines of science, such as Inner loop, Field and Sample, therefore creating a link between diverse domains of science. His work in the fields of Feature learning overlaps with other areas such as Generalization, Reuse and Initialization.
His research integrates issues of Tree, Theoretical computer science, Heuristics and Benchmark in his study of Artificial neural network. The various areas that he examines in his Deep learning study include Pixel and Record locking. The concepts of his Reinforcement learning study are interwoven with issues in Computation, Combinatorial optimization, Leverage and Embodied cognition.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Distilling the Knowledge in a Neural Network
Geoffrey E. Hinton;Oriol Vinyals;Jeffrey Dean.
arXiv: Machine Learning (2015)
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Martín Abadi;Ashish Agarwal;Paul Barham;Eugene Brevdo.
arXiv: Distributed, Parallel, and Cluster Computing (2015)
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Jeff Donahue;Yangqing Jia;Oriol Vinyals;Judy Hoffman.
international conference on machine learning (2014)
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
Yonghui Wu;Mike Schuster;Zhifeng Chen;Quoc V. Le.
arXiv: Computation and Language (2016)
WaveNet: A Generative Model for Raw Audio
Aäron van den Oord;Sander Dieleman;Heiga Zen;Karen Simonyan.
arXiv: Sound (2016)
Representation Learning with Contrastive Predictive Coding
Aaron van den Oord;Yazhe Li;Oriol Vinyals.
arXiv: Learning (2018)
A Neural Conversational Model
Oriol Vinyals;Quoc V. Le.
arXiv: Computation and Language (2015)
Understanding deep learning requires rethinking generalization.
Chiyuan Zhang;Samy Bengio;Moritz Hardt;Benjamin Recht.
international conference on learning representations (2017)
Matching networks for one shot learning
Oriol Vinyals;Charles Blundell;Timothy Lillicrap;Koray Kavukcuoglu.
neural information processing systems (2016)
Beyond short snippets: Deep networks for video classification
Joe Yue-Hei Ng;Matthew Hausknecht;Sudheendra Vijayanarasimhan;Oriol Vinyals.
computer vision and pattern recognition (2015)
Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: