Steffen Remus

Artificial Intelligence · 2015–2019

A researcher in the Science corpus: 3 papers; trusts 33 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 123 citations into it; #30,813 of 35,345 by trust here, #19,439 by citations
• Natural Language Processing — 83 citations into it
• Representation & Self-Supervised Learning — 29 citations into it; #2,803 of 3,145 by trust here, #2,516 by citations
• Information Systems — 22 citations into it
• Computer Vision and Pattern Recognition — 20 citations into it
• Large Language Models — 9 citations into it

Whom Steffen Remus trusts overall

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

1. Tomas Mikolov
2. Geoffrey E. Hinton
3. Marco Baroni
4. Omer Levy
5. Yoshua Bengio
6. Ilya Sutskever
7. Diederik P. Kingma
8. Dekang Lin
9. Alex Krizhevsky
10. Yann LeCun

Who trusts Steffen Remus overall

1. Yogarshi Vyas
2. Mario Giulianelli
3. Abhilasha Ravichander

Papers from Steffen Remus's perspective

Ordered by how strongly the researchers Steffen Remus trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Attention is All you Need (Neural Information Processing Systems)
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Do Supervised Distributional Methods Really Learn Lexical Inference Relations? (2015) — 246 citations
• Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings (2019) — 201 citations
• Entity-Centric Information Access with Human in the Loop for the Biomedical Domain (2017) — 10 citations