Thomas George

Artificial Intelligence · with Laurent · 2018–2021

A researcher in the Science corpus: 3 papers; trusts 160 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 98 citations into it
• Computer Vision and Pattern Recognition — 52 citations into it
• Optimization & Training — 33 citations into it
• Meta & Continual Learning — 27 citations into it
• Model Architectures — 16 citations into it
• Statistical and Nonlinear Physics — 16 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Signal Processing — 6 citations into it

Whom Thomas George trusts overall

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

1. Alex Krizhevsky
2. Karen Simonyan
3. Kaiming He
4. Diederik P. Kingma
5. Geoffrey E. Hinton
6. Shun-ichi Amari
7. Yoshua Bengio
8. Jimmy Ba
9. James Martens
10. Yann LeCun

Who trusts Thomas George overall

1. Janghyeon Lee
2. Yang He
3. Hyounguk Shon
4. Chaoqi Wang
5. Alessandro Achille
6. Erik A. Daxberger
7. Oleksiy Ostapenko
8. Lingao Xiao
9. Roger Baker Grosse
10. Frederik Kunstner

Papers from Thomas George's perspective

Ordered by how strongly the researchers Thomas George 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Fast Approximate Natural Gradient Descent in a Kronecker-factored Eigenbasis (2018) — 210 citations
• Continual Learning in Deep Networks: an Analysis of the Last Layer (2021) — 24 citations
• Implicit Regularization via Neural Feature Alignment (2020) — 71 citations