Sabine Schulte im Walde

Artificial Intelligence · 2013–2018

A researcher in the Science corpus: 5 papers; trusts 104 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 51 citations into it; #21,770 of 35,345 by trust here, #21,878 by citations
• Natural Language Processing — 46 citations into it; #2,398 of 5,688 by trust here, #4,112 by citations
• Representation & Self-Supervised Learning — 21 citations into it; #1,433 of 3,145 by trust here, #2,404 by citations
• Evaluation & Benchmarks — 7 citations into it
• Model Architectures — 6 citations into it

Whom Sabine Schulte im Walde trusts overall

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

1. Tomas Mikolov
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Diederik P. Kingma
5. Ilya Sutskever
6. Richard Socher
7. Alex Krizhevsky
8. Marco Baroni
9. Yann LeCun
10. George A. Miller

Who trusts Sabine Schulte im Walde overall

1. Anna Rogers
2. Haixuan Guo
3. Ira Leviant
4. Aman Madaan
5. Piotr Bojanowski
6. Edouard Grave
7. Joo-Kyung Kim
8. Aleksandr Drozd
9. Fabienne Cap

Papers from Sabine Schulte im Walde's perspective

Ordered by how strongly the researchers Sabine Schulte im Walde trusts endorse each paper. Same corpus, a different order for every researcher.

1. Long Short-Term Memory
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet classification with deep convolutional neural networks
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Integrating Distributional Lexical Contrast into Word Embeddings for Antonym-Synonym Distinction (2016) — 145 citations
• Projecting Embeddings for Domain Adaption: Joint Modeling of Sentiment Analysis in Diverse Domains (2018) — 34 citations
• Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets (2017) — 75 citations
• Multilingual Reliability and “Semantic” Structure of Continuous Word Spaces (2015) — 57 citations
• Using subcategorization knowledge to improve case prediction for translation to German (2013) — 24 citations