Lorenzo Bonicelli

Artificial Intelligence · 2021–2022

A researcher in the Science corpus: 5 papers; trusts 393 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 230 citations into it; #15,927 of 35,345 by trust here, #19,439 by citations
• Computer Vision and Pattern Recognition — 183 citations into it; #10,199 of 21,575 by trust here, #12,992 by citations
• Meta & Continual Learning — 109 citations into it; #428 of 1,041 by trust here, #673 by citations
• Representation & Self-Supervised Learning — 91 citations into it
• Optimization & Training — 40 citations into it
• Model Architectures — 18 citations into it
• Reinforcement Learning — 16 citations into it
• Efficiency & Compression — 14 citations into it

Whom Lorenzo Bonicelli trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Arslan Chaudhry
6. Diederik P. Kingma
7. Marc'Aurelio Ranzato
8. Yann LeCun
9. Ilya Sutskever
10. Ross B. Girshick

Who trusts Lorenzo Bonicelli overall

1. Liyuan Wang
2. Matteo Boschini
3. Liangzu Peng
4. Byeonghwi Kim
5. Xingxing Zhang
6. Emanuele Frascaroli
7. Hang Su
8. Angelo Porrello
9. Paris V. Giampouras
10. Minhyuk Seo

Papers from Lorenzo Bonicelli's perspective

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

Their papers

• Continual semi-supervised learning through contrastive interpolation consistency (2021) — 51 citations
• On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning (2022) — 63 citations
• Class-Incremental Continual Learning Into the eXtended DER-Verse (2022) — 200 citations
• Transfer without Forgetting (2022) — 74 citations
• Effects of Auxiliary Knowledge on Continual Learning (2022) — 9 citations