Georg Wiese
Artificial Intelligence · 2017
A researcher in the Science corpus: 3 papers; trusts 92 researchers and is trusted by 66 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 49 citations into it; #5,701 of 35,345 by trust here, #9,275 by citations
- Natural Language Processing — 42 citations into it; #888 of 5,688 by trust here, #1,735 by citations
- Computer Vision and Pattern Recognition — 18 citations into it; #5,255 of 21,575 by trust here, #6,542 by citations
- Evaluation & Benchmarks — 13 citations into it
- Representation & Self-Supervised Learning — 9 citations into it
- Information Retrieval & Search — 8 citations into it; #299 of 1,022 by trust here, #602 by citations
- Information Systems — 8 citations into it
- Optimization & Training — 7 citations into it
Whom Georg Wiese trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Yoshua Bengio
- Geoffrey E. Hinton
- Jimmy Ba
- Richard Socher
- Sepp Hochreiter
- Ilya Sutskever
- Jeffrey Pennington
- Tomas Mikolov
- Alex Krizhevsky
Who trusts Georg Wiese overall
- Minghao Hu
- Christopher Clark
- Jianfeng Gao
- Dirk Weissenborn
- Nicola De Cao
- Johannes Welbl
- Cheolhyoung Lee
- Debajyoti Chatterjee
- Jonas Pfeiffer
- Kai Sun
Papers from Georg Wiese's perspective
Ordered by how strongly the researchers Georg Wiese 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
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- 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)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Making Neural QA as Simple as Possible but not Simpler (2017) — 220 citations
- Neural Domain Adaptation for Biomedical Question Answering (2017) — 121 citations
- FastQA: A Simple and Efficient Neural Architecture for Question Answering (2017) — 69 citations