Claudio Gambella

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 68 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 100 citations into it
• Computer Vision and Pattern Recognition — 36 citations into it
• Safety, Robustness & Interpretability — 21 citations into it
• Statistics and Probability — 21 citations into it
• Optimization & Training — 17 citations into it
• Computational Theory and Mathematics — 15 citations into it
• Management Science and Operations Research — 15 citations into it
• Atomic and Molecular Physics, and Optics — 12 citations into it

Whom Claudio Gambella trusts overall

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

1. Yann LeCun
2. Dimitris J. Bertsimas
3. Xiaojin Zhu
4. Alex Krizhevsky
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. Kaiming He
8. Kristin P. Bennett
9. Ilya Sutskever
10. Michael Brückner

Who trusts Claudio Gambella overall

1. Mohammad Mustafa Taye

Papers from Claudio Gambella's perspective

Ordered by how strongly the researchers Claudio Gambella 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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

• Quantum Computing for Finance: State-of-the-Art and Future Prospects (2020) — 329 citations
• Optimization Models for Machine Learning: A Survey (2019) — 261 citations