Angelo Porrello

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 11 papers; trusts 770 researchers and is trusted by 42 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 340 citations into it; #4,557 of 35,345 by trust here, #4,656 by citations
• Computer Vision and Pattern Recognition — 298 citations into it; #7,618 of 21,575 by trust here, #8,409 by citations
• Representation & Self-Supervised Learning — 119 citations into it
• Meta & Continual Learning — 104 citations into it; #94 of 1,041 by trust here, #104 by citations
• Optimization & Training — 59 citations into it
• Model Architectures — 42 citations into it
• Evaluation & Benchmarks — 23 citations into it
• Efficiency & Compression — 22 citations into it

Whom Angelo Porrello trusts overall

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

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

Who trusts Angelo Porrello overall

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

Papers from Angelo Porrello's perspective

Ordered by how strongly the researchers Angelo Porrello 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. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
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
• Latent Space Autoregression for Novelty Detection (2018) — 489 citations
• On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning (2022) — 63 citations
• How many Observations are Enough? Knowledge Distillation for Trajectory Forecasting (2022) — 77 citations
• Class-Incremental Continual Learning Into the eXtended DER-Verse (2022) — 200 citations
• Transfer without Forgetting (2022) — 74 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
• Classifying Signals on Irregular Domains via Convolutional Cluster Pooling (2019) — 11 citations
• Spotting Insects from Satellites: Modeling the Presence of Culicoides Imicola Through Deep CNNs (2019) — 14 citations