Davide Boscaini

Computer Vision and Pattern Recognition · 2015–2019

A researcher in the Science corpus: 9 papers; trusts 501 researchers and is trusted by 207 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 223 citations into it; #2,786 of 21,575 by trust here, #2,027 by citations
• Computational Mechanics — 106 citations into it; #186 of 10,001 by trust here, #125 by citations
• Artificial Intelligence — 90 citations into it; #10,542 of 35,345 by trust here, #4,370 by citations
• Graph Learning — 45 citations into it; #192 of 693 by trust here, #84 by citations
• Computer Graphics and Computer-Aided Design — 43 citations into it; #1,031 of 1,584 by trust here, #899 by citations
• Representation & Self-Supervised Learning — 36 citations into it
• Model Architectures — 33 citations into it; #300 of 2,278 by trust here, #306 by citations
• Computational Theory and Mathematics — 28 citations into it; #11,004 of 12,182 by trust here, #2,851 by citations

Whom Davide Boscaini trusts overall

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

1. Geoffrey E. Hinton
2. Yann LeCun
3. Michael M. Bronstein
4. Diederik P. Kingma
5. Alex Krizhevsky
6. A. Bronstein
7. Roee Litman
8. Yoshua Bengio
9. Matthew D. Zeiler
10. Jonathan Masci

Who trusts Davide Boscaini overall

1. Michael M. Bronstein
2. Federico Monti
3. Or Litany
4. Jonathan Masci
5. Emanuele Rodolà
6. Nitika Verma
7. L. Zampieri
8. Zhiwu Huang
9. Adrien Poulenard
10. Matthias Fey

Papers from Davide Boscaini's perspective

Ordered by how strongly the researchers Davide Boscaini 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs (2016) — 1,972 citations
• Learning shape correspondence with anisotropic convolutional neural networks (2016) — 537 citations
• Learning class‐specific descriptors for deformable shapes using localized spectral convolutional networks (2015) — 230 citations
• Anisotropic Diffusion Descriptors (2016) — 132 citations
• Geodesic Convolutional Neural Networks on Riemannian Manifolds (2015) — 789 citations
• Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning (2019) — 804 citations
• Geometric deep learning (2016) — 102 citations
• ShapeNet: Convolutional Neural Networks on Non-Euclidean Manifolds (2015) — 61 citations
• Shape‐from‐Operator: Recovering Shapes from Intrinsic Operators (2015) — 48 citations