Jonas Gehring

Artificial Intelligence · 2013–2023

A researcher in the Science corpus: 6 papers; trusts 192 researchers and is trusted by 320 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 133 citations into it; #1,109 of 35,345 by trust here, #1,310 by citations
• Natural Language Processing — 96 citations into it; #430 of 5,688 by trust here, #390 by citations
• Computer Vision and Pattern Recognition — 55 citations into it; #8,299 of 21,575 by trust here, #7,176 by citations
• Model Architectures — 39 citations into it
• Evaluation & Benchmarks — 13 citations into it
• Optimization & Training — 13 citations into it
• Signal Processing — 11 citations into it; #3,961 of 7,572 by trust here, #4,560 by citations
• Speech & Audio — 5 citations into it

Whom Jonas Gehring trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Ilya Sutskever
4. Kaiming He
5. Diederik P. Kingma
6. Kyunghyun Cho
7. Alex Krizhevsky
8. Yann LeCun
9. Dzmitry Bahdanau
10. Ronan Collobert

Who trusts Jonas Gehring overall

1. Xu Tan
2. Felix Stahlberg
3. Angela Fan
4. David Grangier
5. Myle Ott
6. Michael Auli
7. Jiatao Gu
8. Tao Qin
9. Philipp Koehn
10. Kyunghyun Cho

Papers from Jonas Gehring's perspective

Ordered by how strongly the researchers Jonas Gehring 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Convolutional Sequence to Sequence Learning (2017) — 3,545 citations
• A Convolutional Encoder Model for Neural Machine Translation (2016) — 466 citations
• Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger (2018) — 27 citations
• Code Llama: Open Foundation Models for Code (2023) — 3,394 citations
• Growing Up Together: Structured Exploration for Large Action Spaces (2019) — 5 citations
• Extracting deep bottleneck features using stacked auto-encoders (2013) — 307 citations