Antreas Antoniou
Artificial Intelligence · 2017–2020
A researcher in the Science corpus: 6 papers; trusts 871 researchers and is trusted by 235 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 298 citations into it; #2,725 of 35,345 by trust here, #3,673 by citations
- Computer Vision and Pattern Recognition — 196 citations into it; #2,516 of 21,575 by trust here, #2,729 by citations
- Representation & Self-Supervised Learning — 77 citations into it
- Optimization & Training — 67 citations into it
- Meta & Continual Learning — 61 citations into it; #99 of 1,041 by trust here, #108 by citations
- Reinforcement Learning — 55 citations into it
- Model Architectures — 42 citations into it
- Evaluation & Benchmarks — 26 citations into it
Whom Antreas Antoniou trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Alex Krizhevsky
- Kaiming He
- Yoshua Bengio
- Diederik P. Kingma
- Ilya Sutskever
- Yann LeCun
- Oriol Vinyals
- Sergey Ioffe
- Ruslan Salakhutdinov
Who trusts Antreas Antoniou overall
- Han-Jia Ye
- Ekin Dogus Cubuk
- Timothy M. Hospedales
- Barret Zoph
- Zhenyi Wang
- Zhen Wang
- Massimiliano Patacchiola
- Qianru Sun
- Christian Simon
- Alex Hernandez-Garcia
Papers from Antreas Antoniou's perspective
Ordered by how strongly the researchers Antreas Antoniou trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Data Augmentation Generative Adversarial Networks (2017) — 1,180 citations
- How to train your MAML (2018) — 887 citations
- Meta-Learning in Neural Networks: A Survey (2020) — 2,723 citations
- Defining Benchmarks for Continual Few-Shot Learning (2020) — 42 citations
- Augmenting Image Classifiers Using Data Augmentation Generative Adversarial Networks (2018) — 123 citations
- School of Informatics, University of Edinburgh