Matteo Boschini
Artificial Intelligence · 2020–2023
A researcher in the Science corpus: 8 papers; trusts 622 researchers and is trusted by 39 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 307 citations into it; #4,712 of 35,345 by trust here, #5,120 by citations
- Computer Vision and Pattern Recognition — 249 citations into it; #7,562 of 21,575 by trust here, #9,079 by citations
- Meta & Continual Learning — 143 citations into it; #97 of 1,041 by trust here, #104 by citations
- Representation & Self-Supervised Learning — 125 citations into it
- Optimization & Training — 58 citations into it
- Model Architectures — 27 citations into it
- Reinforcement Learning — 22 citations into it
- Efficiency & Compression — 21 citations into it
Whom Matteo Boschini trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Kaiming He
- Geoffrey E. Hinton
- Arslan Chaudhry
- Yoshua Bengio
- Diederik P. Kingma
- Marc'Aurelio Ranzato
- Yann LeCun
- Ilya Sutskever
- Ross B. Girshick
Who trusts Matteo Boschini overall
- Lorenzo Bonicelli
- Liyuan Wang
- Zhen Wang
- Byeonghwi Kim
- Gyuhak Kim
- Prashant Bhat
- Emanuele Frascaroli
- Zixuan Ke
- Angelo Porrello
- Elahe Arani
Papers from Matteo Boschini's perspective
Ordered by how strongly the researchers Matteo Boschini trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
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
- CaSpeR: Latent Spectral Regularization for Continual Learning (2023) — 12 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
- Rethinking Experience Replay: a Bag of Tricks for Continual Learning (2020) — 185 citations