Sascha Rothe
Artificial Intelligence · 2015–2021
A researcher in the Science corpus: 5 papers; trusts 295 researchers and is trusted by 49 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 162 citations into it; #4,177 of 35,345 by trust here, #7,797 by citations
- Natural Language Processing — 161 citations into it; #1,053 of 5,688 by trust here, #1,857 by citations
- Computer Vision and Pattern Recognition — 34 citations into it; #6,478 of 21,575 by trust here, #12,419 by citations
- Evaluation & Benchmarks — 29 citations into it
- Representation & Self-Supervised Learning — 23 citations into it; #1,080 of 3,145 by trust here, #2,178 by citations
- Large Language Models — 22 citations into it; #158 of 799 by trust here, #555 by citations
- Model Architectures — 17 citations into it
- Information Systems — 9 citations into it
Whom Sascha Rothe trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Ilya Sutskever
- Sepp Hochreiter
- Alex Krizhevsky
- Tomas Mikolov
- Alec Radford
- Richard Socher
- Yann LeCun
Who trusts Sascha Rothe overall
- Mohammad Taher Pilehvar
- Shashi Narayan
- Mihir Kale
- José Camacho-Collados
- Felix Stahlberg
- Joshua Maynez
- Chris Biemann
- Stefano Faralli
- Sven Buechel
- Ignacio Iacobacci
Papers from Sascha Rothe's perspective
Ordered by how strongly the researchers Sascha Rothe trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes (2015) — 290 citations
- Leveraging Pre-trained Checkpoints for Sequence Generation Tasks (2019) — 471 citations
- Ultradense Word Embeddings by Orthogonal Transformation (2016) — 116 citations
- Encode, Tag, Realize: High-Precision Text Editing (2019) — 191 citations
- Focus Attention: Promoting Faithfulness and Diversity in Summarization (2021) — 53 citations