Oriol Vinyals
E46146
Oriol Vinyals is a prominent computer scientist and machine learning researcher known for his influential work on deep learning, sequence-to-sequence models, and reinforcement learning at leading AI labs.
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
artificial intelligence researcher
ⓘ
computer scientist ⓘ machine learning researcher ⓘ |
| activeIn | 21st century ⓘ |
| citizenship | Spain ⓘ |
| educatedAt |
Polytechnic University of Catalonia
ⓘ
surface form:
Universitat Politècnica de Catalunya
University of California, San Diego ⓘ |
| employer |
Google
ⓘ
DeepMind ⓘ
surface form:
Google DeepMind
|
| fieldOfWork |
artificial intelligence
ⓘ
computer vision ⓘ deep learning ⓘ machine learning ⓘ natural language processing ⓘ reinforcement learning ⓘ |
| gender | male ⓘ |
| hasAcademicDegree | PhD in computer science ⓘ |
| hasHIndex | high (among top ML researchers) ⓘ |
| hasRole |
industry researcher
ⓘ
research team leader ⓘ scientific author ⓘ |
| knownFor |
advances in sequence-to-sequence learning
ⓘ
applications of deep learning to games ⓘ contributions to large-scale neural network training ⓘ |
| nativeLanguage | Catalan ⓘ |
| notableFor |
applied deep learning in games
ⓘ
deep learning for NLP ⓘ deep reinforcement learning ⓘ sequence-to-sequence models ⓘ |
| notableWork |
AlphaStar
ⓘ
surface form:
AlphaStar (StarCraft II agent)
Matching Networks for One Shot Learning ⓘ Neural Turing Machines (contributions) ⓘ Pointer Networks ⓘ Sequence to Sequence Learning with Neural Networks ⓘ Show and Tell: A Neural Image Caption Generator ⓘ |
| position |
Principal Scientist
ⓘ
research lead ⓘ |
| publicationVenue |
ACL
ⓘ
IEEE Computer Society Conference on Computer Vision and Pattern Recognition ⓘ
surface form:
CVPR
ICLR ⓘ ICML ⓘ NeurIPS ⓘ |
| researchInterest |
few-shot learning
ⓘ
meta-learning ⓘ neural networks ⓘ representation learning ⓘ sequence modeling ⓘ |
| worksAt |
London, England
ⓘ
surface form:
London (DeepMind headquarters)
|
Referenced by (3)
Full triples — surface form annotated when it differs from this entity's canonical label.