Maximilian Igl

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 8 papers; trusts 178 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 199 citations into it; #8,529 of 35,345 by trust here, #6,614 by citations
• Reinforcement Learning — 140 citations into it; #1,147 of 2,242 by trust here, #1,015 by citations
• Probabilistic & Bayesian Methods — 48 citations into it; #1,388 of 1,525 by trust here, #590 by citations
• Computer Vision and Pattern Recognition — 41 citations into it; #20,574 of 21,575 by trust here, #12,992 by citations
• Management Science and Operations Research — 27 citations into it
• Computational Theory and Mathematics — 24 citations into it
• Generative Models — 21 citations into it; #1,680 of 1,956 by trust here, #1,601 by citations
• Optimization & Training — 20 citations into it

Whom Maximilian Igl trusts overall

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

1. Richard S. Sutton
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Yoshua Bengio
6. Sergey Levine
7. Kaiming He
8. Volodymyr Mnih
9. David Silver
10. Yann LeCun

Who trusts Maximilian Igl overall

1. Annie Xie
2. Amy Zhang
3. Georgios Papoudakis
4. Chelsea Finn
5. Sergey Levine
6. Jianzhun Du
7. James Harrison
8. Luisa M. Zintgraf
9. Alex Kendall
10. Alex X. Lee

Papers from Maximilian Igl's perspective

Ordered by how strongly the researchers Maximilian Igl 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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

• TreeQN and ATreeC: Differentiable Tree Planning for Deep Reinforcement Learning (2017) — 72 citations
• Deep Variational Reinforcement Learning for POMDPs (2018) — 308 citations
• Tighter Variational Bounds are Not Necessarily Better (2018) — 217 citations
• Auto-Encoding Sequential Monte Carlo (2017) — 166 citations
• Multitask Soft Option Learning (2019) — 26 citations
• Transient Non-stationarity and Generalisation in Deep Reinforcement Learning (2020) — 106 citations
• VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning (2019) — 331 citations
• TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning (2017) — 77 citations