Tobias Pohlen

Artificial Intelligence · 2016–2022

A researcher in the Science corpus: 7 papers; trusts 245 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 145 citations into it; #17,373 of 35,345 by trust here, #1,405 by citations
• Reinforcement Learning — 126 citations into it; #2,233 of 2,242 by trust here, #332 by citations
• Computer Vision and Pattern Recognition — 72 citations into it; #12,436 of 21,575 by trust here, #5,116 by citations
• Model Architectures — 26 citations into it
• Management Science and Operations Research — 22 citations into it
• Optimization & Training — 19 citations into it
• Statistics and Probability — 8 citations into it

Whom Tobias Pohlen trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Karen Simonyan
6. Yoshua Bengio
7. Yann LeCun
8. Richard S. Sutton
9. Christian Szegedy
10. Ilya Sutskever

Who trusts Tobias Pohlen overall

1. Eduardo Romera
2. Magnus Wrenninge
3. Liang-Chieh Chen
4. Yassine Ouali
5. Adam W. Harley
6. Hengshuang Zhao
7. José Manuel Álvarez
8. Tianyi Wu
9. Roy Shilkrot
10. J. Liew

Papers from Tobias Pohlen's perspective

Ordered by how strongly the researchers Tobias Pohlen 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Scaling Language Models: Methods, Analysis & Insights from Training Gopher (2021) — 1,640 citations
• Grandmaster level in StarCraft II using multi-agent reinforcement learning (2019) — 4,437 citations
• Observe and Look Further: Achieving Consistent Performance on Atari (2018) — 127 citations
• Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes (2016) — 589 citations
• Reward learning from human preferences and demonstrations in Atari (2018) — 490 citations
• Neural Predictive Belief Representations (2018) — 82 citations
• A data-driven approach for learning to control computers (2022) — 128 citations