Christian Simon
Artificial Intelligence · 2020–2022
A researcher in the Science corpus: 6 papers; trusts 677 researchers and is trusted by 29 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 188 citations into it; #26,661 of 35,345 by trust here, #15,236 by citations
- Computer Vision and Pattern Recognition — 169 citations into it
- Representation & Self-Supervised Learning — 74 citations into it; #1,005 of 3,145 by trust here, #1,540 by citations
- Meta & Continual Learning — 60 citations into it; #220 of 1,041 by trust here, #384 by citations
- Optimization & Training — 26 citations into it
- Radiology, Nuclear Medicine and Imaging — 24 citations into it; #2,875 of 3,265 by trust here, #2,073 by citations
- Reinforcement Learning — 19 citations into it
- Model Architectures — 17 citations into it
Whom Christian Simon trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Kaiming He
- Diederik P. Kingma
- Ilya Sutskever
- Yoshua Bengio
- Yann LeCun
- Ronald J. Williams
- Max Welling
- Ross B. Girshick
Who trusts Christian Simon overall
- Zhen Wang
- Da-Wei Zhou
- Ali Cheraghian
- Marco Toldo
- Shipeng Yan
- Liyuan Wang
- Yaoyao Liu
- Arjun Ashok
- Minsoo Kang
- Jiangwei Xie
Papers from Christian Simon's perspective
Ordered by how strongly the researchers Christian Simon 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)
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Synthesized Feature based Few-Shot Class-Incremental Learning on a Mixture of Subspaces (2021) — 81 citations
- On Generalizing Beyond Domains in Cross-Domain Continual Learning (2022) — 44 citations
- On Learning the Geodesic Path for Incremental Learning (2021) — 155 citations
- Reinforced Attention for Few-Shot Learning and Beyond (2021) — 54 citations
- On Modulating the Gradient for Meta-learning (2020) — 57 citations
- Adaptive Subspaces for Few-Shot Learning (2020) — 425 citations