Martin Längkvist

Computer Vision and Pattern Recognition · 2014–2016

A researcher in the Science corpus: 2 papers; trusts 254 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 51 citations into it
• Artificial Intelligence — 39 citations into it; #21,273 of 35,345 by trust here, #12,083 by citations
• Representation & Self-Supervised Learning — 21 citations into it; #2,149 of 3,145 by trust here, #1,582 by citations
• Signal Processing — 16 citations into it; #4,367 of 7,572 by trust here, #1,669 by citations
• Model Architectures — 15 citations into it
• Media Technology — 14 citations into it
• Natural Language Processing — 9 citations into it
• Management Science and Operations Research — 8 citations into it

Whom Martin Längkvist trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Yann LeCun
4. Alex Krizhevsky
5. Ilya Sutskever
6. Marc'Aurelio Ranzato
7. Alex Graves
8. Honglak Lee
9. L. Eon Bottou
10. David G. Lowe

Who trusts Martin Längkvist overall

1. Omer Berat Sezer
2. John E. Ball
3. Bahareh Pourbabaee
4. Xiaohui Yuan
5. Daifeng Peng
6. Gang Fu
7. Shengdong Du
8. Hongpeng Yin
9. Limin Sun
10. A. Ozbayoglu

Papers from Martin Längkvist's perspective

Ordered by how strongly the researchers Martin Längkvist trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks (2016) — 313 citations
• A review of unsupervised feature learning and deep learning for time-series modeling (2014) — 1,187 citations