Andrei Stoian

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 311 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 163 citations into it; #5,836 of 35,345 by trust here, #5,880 by citations
• Computer Vision and Pattern Recognition — 119 citations into it; #4,718 of 21,575 by trust here, #4,344 by citations
• Meta & Continual Learning — 63 citations into it; #135 of 1,041 by trust here, #129 by citations
• Representation & Self-Supervised Learning — 58 citations into it
• Reinforcement Learning — 42 citations into it
• Generative Models — 30 citations into it; #1,325 of 1,956 by trust here, #865 by citations
• Optimization & Training — 25 citations into it; #1,263 of 2,601 by trust here, #1,904 by citations
• Evaluation & Benchmarks — 19 citations into it

Whom Andrei Stoian trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Diederik P. Kingma
5. Yann LeCun
6. Timothée Lesort
7. Kaiming He
8. Ilya Sutskever
9. Ian J. Goodfellow
10. Radford M. Neal

Who trusts Andrei Stoian overall

1. Timothée Lesort
2. Massimo Caccia
3. Zheda Mai
4. Dongsub Shim
5. Antreas Antoniou
6. Martin Mundt
7. Oleksiy Ostapenko
8. Lucas Caccia
9. Kalifou René Traoré
10. Tongtong Wu

Papers from Andrei Stoian's perspective

Ordered by how strongly the researchers Andrei Stoian 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Generative Models from the perspective of Continual Learning (2018) — 171 citations
• Continual Learning for Robotics (2019) — 310 citations
• Marginal Replay vs Conditional Replay for Continual Learning (2018) — 38 citations
• Regularization Shortcomings for Continual Learning (2019) — 50 citations