Jakob Bauer

Artificial Intelligence · 2021–2023

A researcher in the Science corpus: 2 papers; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 120 citations into it
• Reinforcement Learning — 61 citations into it

Whom Jakob Bauer trusts overall

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

1. J. Urgen Schmidhuber
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Kaiming He
5. Richard S. Sutton
6. Diederik P. Kingma
7. Alex Krizhevsky
8. Alex Graves
9. Yann LeCun
10. Ilya Sutskever

Papers from Jakob Bauer's perspective

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

Their papers

• Open-Ended Learning Leads to Generally Capable Agents (2021) — 230 citations
• Human-Timescale Adaptation in an Open-Ended Task Space (2023) — 163 citations