Jonas Rothfuss

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 4 papers; trusts 83 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 99 citations into it; #13,365 of 35,345 by trust here, #10,798 by citations
• Reinforcement Learning — 56 citations into it; #2,175 of 2,242 by trust here, #1,586 by citations
• Probabilistic & Bayesian Methods — 31 citations into it
• Optimization & Training — 21 citations into it
• Causal Inference & Discovery — 19 citations into it
• Computer Vision and Pattern Recognition — 18 citations into it
• Control and Systems Engineering — 17 citations into it
• Statistics and Probability — 15 citations into it

Whom Jonas Rothfuss trusts overall

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

1. John Schulman
2. Richard S. Sutton
3. Pieter Abbeel
4. Sergey Levine
5. Geoffrey E. Hinton
6. Diederik P. Kingma
7. Ronald J. Williams
8. Radford M. Neal
9. J. Urgen Schmidhuber
10. Yoshua Bengio

Who trusts Jonas Rothfuss overall

1. Annie Xie
2. Ron Dorfman
3. Sae-Young Chung
4. Chelsea Finn
5. Fan Luo
6. Sergey Levine
7. James Harrison
8. Jack Parker-Holder
9. Tianhe Yu
10. Rasool Fakoor

Papers from Jonas Rothfuss's perspective

Ordered by how strongly the researchers Jonas Rothfuss 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Model-Based Reinforcement Learning via Meta-Policy Optimization (2018) — 259 citations
• ProMP: Proximal Meta-Policy Search (2018) — 227 citations
• Variational Causal Networks: Approximate Bayesian Inference over Causal Structures (2021) — 61 citations
• DiBS: Differentiable Bayesian Structure Learning (2021) — 135 citations