Pietro Buzzega

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 6 papers; trusts 368 researchers and is trusted by 35 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 162 citations into it; #4,890 of 35,345 by trust here, #5,390 by citations
• Computer Vision and Pattern Recognition — 143 citations into it; #8,362 of 21,575 by trust here, #8,952 by citations
• Representation & Self-Supervised Learning — 66 citations into it
• Meta & Continual Learning — 61 citations into it; #106 of 1,041 by trust here, #117 by citations
• Optimization & Training — 36 citations into it
• Model Architectures — 19 citations into it
• Evaluation & Benchmarks — 14 citations into it
• Efficiency & Compression — 11 citations into it

Whom Pietro Buzzega trusts overall

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

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

Who trusts Pietro Buzzega overall

1. Matteo Boschini
2. Zhen Wang
3. Gyuhak Kim
4. Prashant Bhat
5. Zixuan Ke
6. Elahe Arani
7. Lorenzo Bonicelli
8. Quang Pham
9. Bahram Zonooz
10. Liu Liu

Papers from Pietro Buzzega's perspective

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

Their papers

• Dark Experience for General Continual Learning: a Strong, Simple Baseline (2020) — 1,381 citations
• Continual semi-supervised learning through contrastive interpolation consistency (2021) — 51 citations
• Class-Incremental Continual Learning Into the eXtended DER-Verse (2022) — 200 citations
• The color out of space: learning self-supervised representations for Earth Observation imagery (2020) — 65 citations
• Rethinking Experience Replay: a Bag of Tricks for Continual Learning (2020) — 185 citations
• Spotting Insects from Satellites: Modeling the Presence of Culicoides Imicola Through Deep CNNs (2019) — 14 citations