Alessandro Sperduti

Artificial Intelligence · 1997–2020

A researcher in the Science corpus: 15 papers; trusts 115 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 76 citations into it; #12,048 of 35,345 by trust here, #16,771 by citations
• Graph Learning — 60 citations into it
• Computer Vision and Pattern Recognition — 46 citations into it; #4,530 of 21,575 by trust here, #6,873 by citations
• Model Architectures — 29 citations into it
• Computational Theory and Mathematics — 23 citations into it; #9,405 of 12,182 by trust here, #7,142 by citations
• Molecular Biology — 23 citations into it
• Representation & Self-Supervised Learning — 21 citations into it; #1,040 of 3,145 by trust here, #1,638 by citations
• Statistical Learning Theory — 20 citations into it

Whom Alessandro Sperduti trusts overall

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

1. Scott E. Fahlman
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Ilya Sutskever
5. Nino Shervashidze
6. Karsten M. Borgwardt
7. Giovanni Da San Martino
8. James Martens
9. S. V. N. Vishwanathan
10. Michael Collins

Who trusts Alessandro Sperduti overall

1. Alessio Micheli
2. Franco Scarselli
3. Barbara Hammer
4. Liva Ralaivola
5. Justin Johnson
6. Werner Uwents
7. Pierre Baldi
8. J. V. Leeuwen
9. Nicoló Navarin
10. Yoshua Bengio

Papers from Alessandro Sperduti's perspective

Ordered by how strongly the researchers Alessandro Sperduti 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. 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. Going deeper with convolutions
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Supervised neural networks for the classification of structures (1997)
• Pre-training Graph Neural Networks with Kernels (2018) — 30 citations
• Analysis of the Internal Representations Developed by Neural Networks for Structures Applied to Quantitative Structure-Activity Relationship Studies of Benzodiazepines (2001) — 76 citations
• A memory efficient graph kernel (2012) — 16 citations
• Suppression of a SARS-CoV-2 outbreak in the Italian municipality of Vo’ (2020) — 897 citations
• Generative Kernels for Tree-Structured Data (2018) — 24 citations
• On Filter Size in Graph Convolutional Networks (2018) — 53 citations
• Tree-Based Kernel for Graphs With Continuous Attributes (2015) — 28 citations
• Ordered Decompositional DAG kernels enhancements (2015) — 22 citations
• Neural Networks for Sequential Data: a Pre-training Approach based on Hidden Markov Models (2015) — 15 citations
• Pre-training of Recurrent Neural Networks via Linear Autoencoders (2014) — 41 citations
• A Tree-Based Kernel for Graphs (2012) — 56 citations
• Process Mining Manifesto (2011) — 1,416 citations
• Learning Nonsparse Kernels by Self-Organizing Maps for Structured Data (2009) — 27 citations
• A general framework for adaptive processing of data structures (1998)