Katharina Kann

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 5 papers; trusts 156 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 141 citations into it; #21,147 of 35,345 by trust here, #8,893 by citations
• Natural Language Processing — 122 citations into it; #4,169 of 5,688 by trust here, #2,422 by citations
• Computer Vision and Pattern Recognition — 56 citations into it
• Evaluation & Benchmarks — 56 citations into it
• Large Language Models — 22 citations into it
• Model Architectures — 19 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• General Social Sciences — 5 citations into it

Whom Katharina Kann trusts overall

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

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

Who trusts Katharina Kann overall

1. Roee Aharoni
2. Xiang Gao
3. Yoav Goldberg
4. Ondrej Dusek

Papers from Katharina Kann's perspective

Ordered by how strongly the researchers Katharina Kann 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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work? (2020) — 210 citations
• English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too (2020) — 69 citations
• Comparative Study of CNN and RNN for Natural Language Processing (2017) — 1,101 citations
• Single-Model Encoder-Decoder with Explicit Morphological Representation for Reinflection (2016) — 94 citations
• PROST: Physical Reasoning about Objects through Space and Time (2021) — 57 citations