Jonas Köhler

Artificial Intelligence · with No'e · 2017–2020

A researcher in the Science corpus: 6 papers; trusts 175 researchers and is trusted by 43 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 83 citations into it; #9,267 of 35,345 by trust here, #7,471 by citations
• Computer Vision and Pattern Recognition — 72 citations into it; #6,557 of 21,575 by trust here, #6,146 by citations
• Generative Models — 61 citations into it; #551 of 1,956 by trust here, #786 by citations
• Probabilistic & Bayesian Methods — 55 citations into it; #611 of 1,525 by trust here, #485 by citations
• Statistical and Nonlinear Physics — 47 citations into it; #1,866 of 10,127 by trust here, #1,085 by citations
• Model Architectures — 31 citations into it; #810 of 2,278 by trust here, #915 by citations
• Molecular Biology — 20 citations into it; #3,678 of 17,773 by trust here, #7,574 by citations
• Signal Processing — 20 citations into it

Whom Jonas Köhler trusts overall

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

1. Diederik P. Kingma
2. Danilo Jimenez Rezende
3. Laurent Dinh
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Frank No'e
7. Max Welling
8. Jimmy Ba
9. Alex Krizhevsky
10. Radford M. Neal

Who trusts Jonas Köhler overall

1. Hao Wu
2. Frank No'e
3. Minkai Xu
4. Leon Klein
5. Emiel Hoogeboom
6. George Papamakarios
7. Risi Kondor
8. Jason Yim
9. Taco Cohen
10. Victor Garcia Satorras

Papers from Jonas Köhler's perspective

Ordered by how strongly the researchers Jonas Köhler 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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

• Spherical CNNs (2018) — 1,032 citations
• Convolutional Networks for Spherical Signals (2017) — 75 citations
• Equivariant Flows: exact likelihood generative learning for symmetric densities (2020) — 351 citations
• Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning (2019) — 873 citations
• Equivariant Flows: sampling configurations for multi-body systems with symmetric energies (2019) — 108 citations
• Stochastic Normalizing Flows (2020) — 224 citations