Paul Micaelli

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 515 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 206 citations into it; #9,560 of 35,345 by trust here, #7,797 by citations
• Computer Vision and Pattern Recognition — 126 citations into it; #6,682 of 21,575 by trust here, #5,620 by citations
• Representation & Self-Supervised Learning — 49 citations into it; #1,389 of 3,145 by trust here, #2,178 by citations
• Meta & Continual Learning — 48 citations into it; #663 of 1,041 by trust here, #286 by citations
• Reinforcement Learning — 37 citations into it
• Optimization & Training — 36 citations into it
• Model Architectures — 21 citations into it
• Efficiency & Compression — 17 citations into it

Whom Paul Micaelli trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Yoshua Bengio
5. Jimmy Ba
6. Kaiming He
7. Yann LeCun
8. Ilya Sutskever
9. J. Urgen Schmidhuber
10. Alex Graves

Who trusts Paul Micaelli overall

1. Pratyush Maini
2. Lin Wang
3. Aditya Golatkar
4. Jianping Gou
5. Liangchen Luo
6. Kalpesh Krishna
7. Huan Liu
8. Kuk-Jin Yoon
9. Alessandro Achille
10. Matthew Jagielski

Papers from Paul Micaelli's perspective

Ordered by how strongly the researchers Paul Micaelli 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. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Meta-Learning in Neural Networks: A Survey (2020) — 2,723 citations
• Zero-shot Knowledge Transfer via Adversarial Belief Matching (2019) — 257 citations