Alessio Micheli

Artificial Intelligence · 2001–2019

A researcher in the Science corpus: 6 papers; trusts 123 researchers and is trusted by 49 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 100 citations into it; #3,853 of 35,345 by trust here, #10,319 by citations
• Graph Learning — 70 citations into it; #111 of 693 by trust here, #366 by citations
• Computer Vision and Pattern Recognition — 48 citations into it; #18,785 of 21,575 by trust here, #12,689 by citations
• Molecular Biology — 31 citations into it
• Model Architectures — 28 citations into it
• Statistical and Nonlinear Physics — 25 citations into it
• Representation & Self-Supervised Learning — 18 citations into it
• Computational Theory and Mathematics — 15 citations into it; #9,406 of 12,182 by trust here, #7,142 by citations

Whom Alessio Micheli trusts overall

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

1. Alessandro Sperduti
2. Scott E. Fahlman
3. Paolo Frasconi
4. Marco Gori
5. Yoshua Bengio
6. Geoffrey E. Hinton
7. Diederik P. Kingma
8. Pierre Baldi
9. Thomas Gärtner
10. Monica Bianchini

Who trusts Alessio Micheli overall

1. Federico Errica
2. Davide Bacciu
3. Werner Uwents
4. Dinh Van Tran
5. Kenneth Marino
6. Xu Yang
7. Justin Gilmer
8. Yueen Ma
9. Zewen Li
10. Min Shi

Papers from Alessio Micheli's perspective

Ordered by how strongly the researchers Alessio Micheli 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. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Neural Network for Graphs: A Contextual Constructive Approach (2009) — 663 citations
• A Fair Comparison of Graph Neural Networks for Graph Classification (2019) — 527 citations
• A Gentle Introduction to Deep Learning for Graphs (2019) — 326 citations
• Analysis of the Internal Representations Developed by Neural Networks for Structures Applied to Quantitative Structure-Activity Relationship Studies of Benzodiazepines (2001) — 76 citations
• Generative Kernels for Tree-Structured Data (2018) — 24 citations
• Deep reservoir computing: A critical experimental analysis (2017) — 485 citations