Riccardo Grazzi

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 17 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 36 citations into it; #13,932 of 35,345 by trust here, #11,072 by citations
• Representation & Self-Supervised Learning — 18 citations into it
• Computer Vision and Pattern Recognition — 16 citations into it; #12,378 of 21,575 by trust here, #9,305 by citations
• Optimization & Training — 16 citations into it
• Computational Mechanics — 13 citations into it
• Management Science and Operations Research — 7 citations into it; #7,818 of 11,946 by trust here, #6,024 by citations

Whom Riccardo Grazzi trusts overall

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

1. Jonathan Baxter
2. Andreas Maurer
3. Anastasia Pentina
4. Yoshua Bengio
5. Geoffrey E. Hinton
6. Shai Shalev-Shwartz
7. Kaiming He
8. Giulia Denevi
9. Alex Krizhevsky
10. Diederik P. Kingma

Who trusts Riccardo Grazzi overall

1. Mikhail Khodak
2. Nilesh Tripuraneni

Papers from Riccardo Grazzi's perspective

Ordered by how strongly the researchers Riccardo Grazzi 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. Attention is All you Need (Neural Information Processing Systems)
4. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Going deeper with convolutions

Their papers

• Bilevel Programming for Hyperparameter Optimization and Meta-Learning (2018) — 888 citations
• Learning-to-Learn Stochastic Gradient Descent with Biased Regularization (2019) — 120 citations