Sune Lehmann
Statistical and Nonlinear Physics · 2009–2020
A researcher in the Science corpus: 5 papers; trusts 23 researchers and is trusted by 35 researchers here, as inferred from the citation graph.
Subfields
- Statistical and Nonlinear Physics — 61 citations into it; #931 of 10,127 by trust here, #735 by citations
- Artificial Intelligence — 42 citations into it; #22,500 of 35,345 by trust here, #12,244 by citations
- Transportation — 28 citations into it
- Molecular Biology — 24 citations into it; #1,654 of 17,773 by trust here, #7,298 by citations
- Computer Networks and Communications — 14 citations into it
- Computer Vision and Pattern Recognition — 10 citations into it
- Privacy & Security — 10 citations into it
- Economics and Econometrics — 9 citations into it
Whom Sune Lehmann trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Mark Newman
- Aaron Clauset
- Yoshua Bengio
- Diederik P. Kingma
- Ilya Sutskever
- Michael T. Gastner
- Geoffrey E. Hinton
- Michelle Girvan
- Sepp Hochreiter
- Alex Graves
Who trusts Sune Lehmann overall
- Jaewon Yang
- Jacob Eisenstein
- Jure Leskovec
- Momin M. Malik
- Olaf Sporns
- Julian McAuley
- Jierui Xie
- Jürgen Pfeffer
- Santo Fortunato
- Umashanthi Pavalanathan
Papers from Sune Lehmann's perspective
Ordered by how strongly the researchers Sune Lehmann trusts endorse each paper. Same corpus, a different order for every researcher.
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Understanding the Demographics of Twitter Users (2011) — 920 citations
- Link communities reveal multiscale complexity in networks (2009) — 1,946 citations
- Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle (2020) — 620 citations
- Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm (2017) — 813 citations
- Measuring Large-Scale Social Networks with High Resolution (2014) — 398 citations