Matthias De Lange

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 7 papers; trusts 609 researchers and is trusted by 44 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 262 citations into it; #5,546 of 35,345 by trust here, #3,412 by citations
• Computer Vision and Pattern Recognition — 219 citations into it; #16,440 of 21,575 by trust here, #2,888 by citations
• Meta & Continual Learning — 122 citations into it; #76 of 1,041 by trust here, #22 by citations
• Representation & Self-Supervised Learning — 82 citations into it
• Optimization & Training — 59 citations into it
• Reinforcement Learning — 38 citations into it
• Model Architectures — 27 citations into it
• Efficiency & Compression — 20 citations into it

Whom Matthias De Lange trusts overall

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

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

Who trusts Matthias De Lange overall

1. Da-Wei Zhou
2. Marc Masana
3. Zixuan Ke
4. Fei Ye
5. Yaoyao Liu
6. Marco Toldo
7. Gobinda Saha
8. Liyuan Wang
9. Qiwen Wang
10. Xialei Liu

Papers from Matthias De Lange's perspective

Ordered by how strongly the researchers Matthias De Lange 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. 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

• A Continual Learning Survey: Defying Forgetting in Classification Tasks (2019) — 2,435 citations
• Avalanche: an End-to-End Library for Continual Learning (2021) — 219 citations
• 3rd Continual Learning Workshop Challenge on Egocentric Category and Instance Level Object Understanding (2022) — 7 citations
• Rehearsal revealed: The limits and merits of revisiting samples in continual learning (2021) — 127 citations
• Continual Prototype Evolution: Learning Online from Non-Stationary Data Streams (2020) — 265 citations
• Unsupervised Model Personalization While Preserving Privacy and Scalability: An Open Problem (2020) — 26 citations
• Continual learning: A comparative study on how to defy forgetting in classification tasks (2019) — 260 citations