Filippo Maria Bianchi

Artificial Intelligence · 2019

A researcher in the Science corpus: 2 papers; trusts 58 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 50 citations into it; #30,695 of 35,345 by trust here, #28,481 by citations
• Graph Learning — 49 citations into it
• Computer Vision and Pattern Recognition — 30 citations into it
• Statistical and Nonlinear Physics — 19 citations into it; #8,615 of 10,127 by trust here, #7,402 by citations
• Computational Mechanics — 8 citations into it
• Molecular Biology — 7 citations into it
• Computational Theory and Mathematics — 5 citations into it

Whom Filippo Maria Bianchi trusts overall

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

1. Joan Bruna
2. Inderjit S. Dhillon
3. Yann LeCun
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Michaël Defferrard
7. Ulrike von Luxburg
8. David I. Shuman
9. Thomas Kipf
10. Diederik P. Kingma

Who trusts Filippo Maria Bianchi overall

1. Xiyuan Wang
2. Anton Tsitsulin
3. Mingguo He
4. Emanuele Rossi
5. Muhan Zhang
6. William L. Hamilton
7. John Palowitch
8. Daniele Grattarola
9. Zhewei Wei
10. Bryan Perozzi

Papers from Filippo Maria Bianchi's perspective

Ordered by how strongly the researchers Filippo Maria Bianchi 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

• Spectral Clustering with Graph Neural Networks for Graph Pooling (2019) — 551 citations
• Graph Neural Networks With Convolutional ARMA Filters (2019) — 500 citations