Tobias Pfaff

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 186 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 123 citations into it; #20,840 of 35,345 by trust here, #2,975 by citations
• Reinforcement Learning — 90 citations into it
• Computer Vision and Pattern Recognition — 73 citations into it
• Control and Systems Engineering — 33 citations into it
• Computational Mechanics — 18 citations into it
• Representation & Self-Supervised Learning — 18 citations into it
• Optimization & Training — 13 citations into it
• Computational Theory and Mathematics — 12 citations into it

Whom Tobias Pfaff trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Yoshua Bengio
5. Kaiming He
6. Yann LeCun
7. Ilya Sutskever
8. Jimmy Ba
9. Andrew Zisserman
10. Max Welling

Who trusts Tobias Pfaff overall

1. Zijie Li
2. Johannes Brandstetter
3. Zong-Yi Li
4. Lyle Regenwetter
5. Roberta Raileanu
6. Kazem Meidani
7. Andrew Hundt
8. Nikola B. Kovachki
9. Carles Gelada
10. Rob D. Hesselink

Papers from Tobias Pfaff's perspective

Ordered by how strongly the researchers Tobias Pfaff 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Grandmaster level in StarCraft II using multi-agent reinforcement learning (2019) — 4,437 citations
• Playing hard exploration games by watching YouTube (2018) — 286 citations
• Learning to Simulate Complex Physics with Graph Networks (2020) — 1,550 citations
• One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL (2018) — 26 citations
• Learning Mesh-Based Simulation with Graph Networks (2020) — 1,318 citations