Gabriele Monfardini

Model Architectures · 2005–2011

A researcher in the Science corpus: 5 papers; trusts 19 researchers and is trusted by 499 researchers here, as inferred from the citation graph.

Subfields

• Model Architectures — 23 citations into it; #2,105 of 2,278 by trust here, #2,111 by citations
• Artificial Intelligence — 22 citations into it; #1,454 of 35,345 by trust here, #1,441 by citations
• Representation & Self-Supervised Learning — 13 citations into it
• Computer Vision and Pattern Recognition — 12 citations into it; #2,085 of 21,575 by trust here, #1,461 by citations
• Optimization & Training — 9 citations into it
• Graph Learning — 8 citations into it; #33 of 693 by trust here, #22 by citations
• Natural Language Processing — 8 citations into it
• Cognitive Science & Psychology — 5 citations into it

Whom Gabriele Monfardini trusts overall

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

1. Fernando J. Pineda
2. Marco Gori
3. Alessandro Sperduti
4. Alessio Micheli
5. Franco Scarselli
6. Martin A. Riedmiller
7. Jon M. Kleinberg
8. Paul J. Werbos
9. Paolo Frasconi
10. Barbara Hammer

Who trusts Gabriele Monfardini overall

1. William L. Hamilton
2. Yujia Li
3. Xavier Bresson
4. Qi Liu
5. Justin Johnson
6. Yoshua Bengio
7. Thomas Kipf
8. Joan Bruna
9. Ruslan Salakhutdinov
10. Renjie Liao

Papers from Gabriele Monfardini's perspective

Ordered by how strongly the researchers Gabriele Monfardini 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Going deeper with convolutions
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
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. The Graph Neural Network Model

Their papers

• The Graph Neural Network Model (2009) — 9,596 citations
• A new model for learning in graph domains (2005) — 2,171 citations
• Computational Capabilities of Graph Neural Networks (2009) — 234 citations
• A Comparison between Recursive Neural Networks and Graph Neural Networks (2006) — 40 citations
• Neural networks for relational learning: an experimental comparison (2011) — 39 citations