Lukás Hermann

Artificial Intelligence · 2021–2022

A researcher in the Science corpus: 2 papers; trusts 24 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 41 citations into it; #10,316 of 35,345 by trust here, #14,181 by citations
• Computer Vision and Pattern Recognition — 29 citations into it; #7,079 of 21,575 by trust here, #9,722 by citations
• Reinforcement Learning — 28 citations into it; #2,191 of 2,242 by trust here, #1,822 by citations
• Natural Language Processing — 17 citations into it
• Control and Systems Engineering — 13 citations into it
• Representation & Self-Supervised Learning — 7 citations into it
• Generative Models — 5 citations into it

Whom Lukás Hermann trusts overall

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

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

Who trusts Lukás Hermann overall

1. Moritz Reuss
2. Kevin Black
3. Hongtao Wu
4. Xinghang Li
5. Oier Mees
6. Mohit Shridhar
7. O. Team
8. Maximilian Xiling Li
9. Ted Xiao
10. Ya Jing

Papers from Lukás Hermann's perspective

Ordered by how strongly the researchers Lukás Hermann 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• What Matters in Language Conditioned Robotic Imitation Learning Over Unstructured Data (2022) — 209 citations
• CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks (2021) — 665 citations