Simone Calderara

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 16 papers; trusts 973 researchers and is trusted by 58 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 456 citations into it; #3,597 of 35,345 by trust here, #3,304 by citations
• Computer Vision and Pattern Recognition — 422 citations into it; #5,804 of 21,575 by trust here, #4,212 by citations
• Meta & Continual Learning — 165 citations into it; #96 of 1,041 by trust here, #95 by citations
• Representation & Self-Supervised Learning — 158 citations into it; #1,589 of 3,145 by trust here, #1,277 by citations
• Optimization & Training — 80 citations into it
• Model Architectures — 60 citations into it
• Reinforcement Learning — 34 citations into it
• Efficiency & Compression — 33 citations into it

Whom Simone Calderara 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. Marc'Aurelio Ranzato
9. Ross B. Girshick
10. David G. Lowe

Who trusts Simone Calderara overall

1. Gyuhak Kim
2. Matteo Boschini
3. Lorenzo Bonicelli
4. Zhen Wang
5. Zixuan Ke
6. Prashant Bhat
7. Liyuan Wang
8. Angelo Porrello
9. Elahe Arani
10. Yiduo Guo

Papers from Simone Calderara's perspective

Ordered by how strongly the researchers Simone Calderara 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

• Conditional Channel Gated Networks for Task-Aware Continual Learning (2020) — 208 citations
• Avalanche: an End-to-End Library for Continual Learning (2021) — 219 citations
• 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
• CaSpeR: Latent Spectral Regularization for Continual Learning (2023) — 12 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
• Effects of Auxiliary Knowledge on Continual Learning (2022) — 9 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
• Predicting the Driver's Focus of Attention: The DR(eye)VE Project (2017) — 322 citations