Simon Schug

Artificial Intelligence · 2021

A researcher in the Science corpus: 2 papers; trusts 21 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 48 citations into it
• Computer Vision and Pattern Recognition — 35 citations into it
• Optimization & Training — 24 citations into it
• Meta & Continual Learning — 16 citations into it
• Representation & Self-Supervised Learning — 14 citations into it

Whom Simon Schug trusts overall

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

1. Yoshua Bengio
2. Ferenc Huszár
3. Kaiming He
4. Xavier Glorot
5. Alex Krizhevsky
6. Geoffrey E. Hinton
7. Yann LeCun
8. Diederik P. Kingma
9. X. Zhang
10. Ilya Sutskever

Who trusts Simon Schug overall

1. Liyuan Wang
2. Xingxing Zhang

Papers from Simon Schug's perspective

Ordered by how strongly the researchers Simon Schug trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Learning where to learn: Gradient sparsity in meta and continual learning (2021) — 60 citations
• Presynaptic stochasticity improves energy efficiency and helps alleviate the stability-plasticity dilemma (2021) — 20 citations