Fabrizio Frasca

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 6 papers; trusts 83 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 205 citations into it; #31,559 of 35,345 by trust here, #14,181 by citations
• Graph Learning — 140 citations into it
• Computer Vision and Pattern Recognition — 79 citations into it
• Statistical and Nonlinear Physics — 74 citations into it; #7,298 of 10,127 by trust here, #2,213 by citations
• Molecular Biology — 48 citations into it
• Computational Theory and Mathematics — 45 citations into it
• Model Architectures — 42 citations into it
• Information Systems — 31 citations into it

Whom Fabrizio Frasca trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yann LeCun
4. Yoshua Bengio
5. Thomas Kipf
6. Alex Krizhevsky
7. Kaiming He
8. Max Welling
9. Jimmy Ba
10. Ilya Sutskever

Who trusts Fabrizio Frasca overall

1. Vijay Prakash Dwivedi
2. Cristian Bodnar
3. Kenneth Atz
4. Ladislav Rampášek
5. Van-Hoang Nguyen
6. Yingmin Wu

Papers from Fabrizio Frasca's perspective

Ordered by how strongly the researchers Fabrizio Frasca 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. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• SIGN: Scalable Inception Graph Neural Networks (2020) — 475 citations
• Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting (2020) — 547 citations
• Fake News Detection on Social Media using Geometric Deep Learning (2019) — 566 citations
• Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks (2021) — 342 citations
• Weisfeiler and Lehman Go Cellular: CW Networks (2021) — 317 citations
• Temporal Graph Networks for Deep Learning on Dynamic Graphs (2020) — 1,099 citations