Stephan Gouws

Artificial Intelligence · 2014–2018

A researcher in the Science corpus: 7 papers; trusts 167 researchers and is trusted by 184 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 153 citations into it; #1,835 of 35,345 by trust here, #564 by citations
• Natural Language Processing — 107 citations into it; #814 of 5,688 by trust here, #163 by citations
• Computer Vision and Pattern Recognition — 64 citations into it; #3,912 of 21,575 by trust here, #505 by citations
• Model Architectures — 45 citations into it; #1,322 of 2,278 by trust here, #595 by citations
• Representation & Self-Supervised Learning — 29 citations into it; #764 of 3,145 by trust here, #1,507 by citations
• Optimization & Training — 13 citations into it
• Evaluation & Benchmarks — 11 citations into it
• Generative Models — 6 citations into it

Whom Stephan Gouws trusts overall

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

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

Who trusts Stephan Gouws overall

1. Yoav Goldberg
2. Sebastian Ruder
3. Mikel Artetxe
4. Yi Tay
5. Ivan Vulic
6. Holger Schwenk
7. Kyunghyun Cho
8. Aditya Mogadala
9. Xinjie Zhou
10. Zhilin Yang

Papers from Stephan Gouws's perspective

Ordered by how strongly the researchers Stephan Gouws 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. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation (2016) — 7,325 citations
• Universal Transformers (2018) — 980 citations
• Tensor2Tensor for Neural Machine Translation (2018) — 553 citations
• BilBOWA: Fast Bilingual Distributed Representations without Word Alignments (2014) — 396 citations
• Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models (2017) — 218 citations
• Fidelity-Weighted Learning (2017) — 78 citations
• Generating Long and Diverse Responses with Neural Conversation Models (2017) — 65 citations