Francesco Masulli

Artificial Intelligence · 2002–2008

A researcher in the Science corpus: 2 papers; trusts 40 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 23 citations into it; #13,541 of 35,345 by trust here, #15,866 by citations
• Computer Vision and Pattern Recognition — 17 citations into it; #14,360 of 21,575 by trust here, #12,172 by citations
• Statistical Learning Theory — 14 citations into it
• Optimization & Training — 9 citations into it
• Statistics and Probability — 9 citations into it
• Evaluation & Benchmarks — 7 citations into it
• Model Architectures — 6 citations into it
• Representation & Self-Supervised Learning — 6 citations into it

Whom Francesco Masulli trusts overall

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

1. Robert E. Schapire
2. Jerome H. Friedman
3. Yoav Freund
4. Thomas G. Dietterich
5. Leo Breiman
6. J. Ross Quinlan
7. Yoshua Bengio
8. Inderjit S. Dhillon
9. Tin Kam Ho
10. Trevor J. Hastie

Who trusts Francesco Masulli overall

1. Arthur Zimek
2. Eugene Tuv
3. Francis R. Bach
4. Maria Brbic

Papers from Francesco Masulli's perspective

Ordered by how strongly the researchers Francesco Masulli 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. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• A survey of kernel and spectral methods for clustering (2008) — 911 citations
• Ensembles of Learning Machines (2002) — 330 citations