Marc Finzi

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 5 papers; trusts 306 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 118 citations into it; #12,956 of 35,345 by trust here, #9,870 by citations
• Computer Vision and Pattern Recognition — 95 citations into it; #9,745 of 21,575 by trust here, #9,305 by citations
• Optimization & Training — 36 citations into it
• Representation & Self-Supervised Learning — 31 citations into it; #1,469 of 3,145 by trust here, #1,692 by citations
• Generative Models — 27 citations into it
• Model Architectures — 26 citations into it; #1,706 of 2,278 by trust here, #1,774 by citations
• Management Science and Operations Research — 17 citations into it
• Signal Processing — 15 citations into it

Whom Marc Finzi trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Taco Cohen
4. Alex Krizhevsky
5. Yoshua Bengio
6. Kaiming He
7. Yann LeCun
8. Max Welling
9. Ilya Sutskever
10. Jimmy Ba

Who trusts Marc Finzi overall

1. David W. Romero
2. Victor Garcia Satorras
3. Sudhanshu Mittal
4. Pavel Izmailov
5. Alexander Kolesnikov
6. Johannes Brandstetter
7. Jiaxiang Wu
8. Emiel Hoogeboom
9. Xiaohua Zhai
10. Octavian-Eugen Ganea

Papers from Marc Finzi's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. 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

• There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average (2018) — 262 citations
• Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data (2020) — 396 citations
• Semi-Supervised Learning with Normalizing Flows (2019) — 137 citations
• Large Language Models Are Zero-Shot Time Series Forecasters (2023) — 788 citations
• Improving Consistency-Based Semi-Supervised Learning with Weight Averaging (2018) — 36 citations