Federico Baldassarre

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 104 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it; #15,835 of 35,345 by trust here, #25,040 by citations
• Molecular Biology — 36 citations into it
• Materials Chemistry — 28 citations into it; #3,243 of 11,513 by trust here, #9,927 by citations
• Graph Learning — 27 citations into it
• Computer Vision and Pattern Recognition — 24 citations into it
• Computational Theory and Mathematics — 19 citations into it
• Computational Biology & Bioinformatics — 17 citations into it
• Safety, Robustness & Interpretability — 13 citations into it

Whom Federico Baldassarre trusts overall

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

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

Who trusts Federico Baldassarre overall

1. Hao Yuan
2. Weiwei Jiang
3. Haiyang Yu
4. Vignesh Ram Somnath
5. Jiayun Luo
6. Xiaoxiao Li
7. Zuobai Zhang
8. Pedro Hermosilla
9. Shuiwang Ji
10. Charlotte Bunne

Papers from Federico Baldassarre's perspective

Ordered by how strongly the researchers Federico Baldassarre 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Going deeper with convolutions
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• GraphQA: protein model quality assessment using graph convolutional networks (2020) — 127 citations
• Explainability Techniques for Graph Convolutional Networks (2019) — 335 citations