Marcello Benedetti

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 24 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 91 citations into it; #19,938 of 35,345 by trust here, #11,490 by citations
• Atomic and Molecular Physics, and Optics — 40 citations into it; #3,478 of 7,135 by trust here, #2,988 by citations
• Computational Theory and Mathematics — 15 citations into it
• Optimization & Training — 8 citations into it

Whom Marcello Benedetti trusts overall

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

1. Edward Farhi
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Maria Schuld
5. Yann LeCun
6. Jimmy Ba
7. Yoshua Bengio
8. Lester James Validad Miranda
9. David E. Rumelhart
10. Alex Graves

Who trusts Marcello Benedetti overall

1. Ryan Larose
2. Maria Schuld
3. Oscar Higgott
4. Hanrui Wang
5. Brian Coyle
6. Edward Grant
7. M. Cerezo
8. Daochen Wang
9. Tak Hur

Papers from Marcello Benedetti's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Parameterized quantum circuits as machine learning models (2019) — 1,332 citations
• An initialization strategy for addressing barren plateaus in parametrized quantum circuits (2019) — 514 citations
• A generative modeling approach for benchmarking and training shallow quantum circuits (2018) — 414 citations
• Hierarchical quantum classifiers (2018) — 381 citations