Lorenzo Pellegrini

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 6 papers; trusts 286 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 135 citations into it; #14,307 of 35,345 by trust here, #10,065 by citations
• Computer Vision and Pattern Recognition — 121 citations into it; #9,278 of 21,575 by trust here, #7,092 by citations
• Meta & Continual Learning — 59 citations into it; #447 of 1,041 by trust here, #312 by citations
• Representation & Self-Supervised Learning — 32 citations into it
• Model Architectures — 28 citations into it
• Optimization & Training — 26 citations into it
• Evaluation & Benchmarks — 20 citations into it
• Reinforcement Learning — 18 citations into it

Whom Lorenzo Pellegrini trusts overall

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

1. Vincenzo Lomonaco
2. Davide Maltoni
3. Kaiming He
4. Alex Krizhevsky
5. Sergey Ioffe
6. Geoffrey E. Hinton
7. Ross B. Girshick
8. Diederik P. Kingma
9. Yoshua Bengio
10. Yann LeCun

Who trusts Lorenzo Pellegrini overall

1. Vincenzo Lomonaco
2. German Ignacio Parisi
3. Leonardo Ravaglia
4. Yabin Wang
5. Magdalena Biesialska
6. Zhiwu Huang
7. Fan Lyu
8. Chuqiao Li
9. Dingcheng Li
10. Manuele Rusci

Papers from Lorenzo Pellegrini's perspective

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

Their papers

• Avalanche: an End-to-End Library for Continual Learning (2021) — 219 citations
• Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches (2019) — 74 citations
• Latent Replay for Real-Time Continual Learning (2019) — 193 citations
• 3rd Continual Learning Workshop Challenge on Egocentric Category and Instance Level Object Understanding (2022) — 7 citations
• CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future Directions (2020) — 54 citations
• Fine-Grained Continual Learning (2019) — 26 citations