Avital Oliver

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 8 papers; trusts 517 researchers and is trusted by 309 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 197 citations into it; #5,824 of 35,345 by trust here, #4,254 by citations
• Computer Vision and Pattern Recognition — 187 citations into it; #2,462 of 21,575 by trust here, #3,239 by citations
• Representation & Self-Supervised Learning — 105 citations into it; #293 of 3,145 by trust here, #290 by citations
• Optimization & Training — 47 citations into it
• Model Architectures — 28 citations into it
• Generative Models — 25 citations into it
• Reinforcement Learning — 23 citations into it
• Natural Language Processing — 13 citations into it

Whom Avital Oliver trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Diederik P. Kingma
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Kaiming He
5. Yoshua Bengio
6. Ilya Sutskever
7. Yann LeCun
8. Ian J. Goodfellow
9. Thorsten Joachims
10. Alex Graves

Who trusts Avital Oliver overall

1. David Berthelot
2. Kihyuk Sohn
3. Xiaohua Zhai
4. Quoc V. Le
5. Nicholas Carlini
6. Yassine Ouali
7. Qizhe Xie
8. Junnan Li
9. Zizhao Zhang
10. Geoff French

Papers from Avital Oliver's perspective

Ordered by how strongly the researchers Avital Oliver trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• S4L: Self-Supervised Semi-Supervised Learning (2019) — 864 citations
• Scaling Vision Transformers to 22 Billion Parameters (2023) — 911 citations
• Teacher–Student Curriculum Learning (2017) — 471 citations
• MetNet: A Neural Weather Model for Precipitation Forecasting (2020) — 361 citations
• Milking CowMask for Semi-Supervised Image Classification (2020) — 60 citations
• S$^\mathbf{4}$L: Self-Supervised Semi-Supervised Learning (2019)
• MixMatch: A Holistic Approach to Semi-Supervised Learning (2019) — 3,592 citations
• Realistic Evaluation of Semi-Supervised Learning Algorithms (2018) — 866 citations