Pietro Lio'

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 8 papers; trusts 201 researchers and is trusted by 143 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 183 citations into it; #2,085 of 35,345 by trust here, #1,023 by citations
• Graph Learning — 127 citations into it; #60 of 693 by trust here, #13 by citations
• Computer Vision and Pattern Recognition — 85 citations into it; #18,406 of 21,575 by trust here, #14,478 by citations
• Representation & Self-Supervised Learning — 46 citations into it; #2,472 of 3,145 by trust here, #1,109 by citations
• Materials Chemistry — 42 citations into it; #394 of 11,513 by trust here, #679 by citations
• Model Architectures — 42 citations into it; #340 of 2,278 by trust here, #151 by citations
• Computational Theory and Mathematics — 41 citations into it
• Statistical and Nonlinear Physics — 40 citations into it; #7,221 of 10,127 by trust here, #1,059 by citations

Whom Pietro Lio' trusts overall

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

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

Who trusts Pietro Lio' overall

1. Matthias Fey
2. Vijay Prakash Dwivedi
3. Yoshua Bengio
4. William L. Hamilton
5. Yanqiao Zhu
6. Weihua Hu
7. Rex Ying
8. Jure Leskovec
9. Yixin Liu
10. Jiaxuan You

Papers from Pietro Lio''s perspective

Ordered by how strongly the researchers Pietro Lio' 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Graph Attention Networks (2017) — 27,282 citations
• Deep Graph Infomax (2018) — 3,051 citations
• Principal Neighbourhood Aggregation for Graph Nets (2020) — 873 citations
• Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks (2021) — 342 citations
• Weisfeiler and Lehman Go Cellular: CW Networks (2021) — 317 citations
• Structure-aware generation of drug-like molecules (2021) — 21 citations
• Probabilistic Dual Network Architecture Search on Graphs (2020) — 27 citations
• Towards Sparse Hierarchical Graph Classifiers (2018) — 291 citations