Bernhard Kratzwald

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 61 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it; #25,655 of 35,345 by trust here, #20,377 by citations
• Natural Language Processing — 29 citations into it; #4,230 of 5,688 by trust here, #3,977 by citations
• Information Retrieval & Search — 16 citations into it; #919 of 1,022 by trust here, #637 by citations
• Computer Vision and Pattern Recognition — 13 citations into it; #19,586 of 21,575 by trust here, #14,151 by citations
• Information Systems — 7 citations into it; #7,576 of 8,771 by trust here, #4,188 by citations

Whom Bernhard Kratzwald trusts overall

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

1. Danqi Chen
2. Pranav Rajpurkar
3. Jason Aaron Edward Weston
4. Adam Fisch
5. Geoffrey E. Hinton
6. Diederik P. Kingma
7. Yoshua Bengio
8. Minjoon Seo
9. Petr Baudis
10. Kenton C. T. Lee

Who trusts Bernhard Kratzwald overall

1. Peng Qi
2. Xiaoman Pan
3. Fengbin Zhu
4. Stefan Feuerriegel
5. Yixin Nie
6. Kai Sun
7. Xiaowen Lin
8. Kenton C. T. Lee
9. Wenqiang Lei
10. Youngjin Jang

Papers from Bernhard Kratzwald's perspective

Ordered by how strongly the researchers Bernhard Kratzwald 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. Attention is All you Need (Neural Information Processing Systems)
3. ImageNet classification with deep convolutional neural networks
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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Gradient-based learning applied to document recognition
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Adaptive Document Retrieval for Deep Question Answering (2018) — 56 citations
• Learning from On-Line User Feedback in Neural Question Answering on the Web (2019) — 21 citations