Markus Svensén

Artificial Intelligence · 1998–2020

A researcher in the Science corpus: 4 papers; trusts 102 researchers and is trusted by 57 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 76 citations into it; #9,472 of 35,345 by trust here, #21,316 by citations
• Computer Vision and Pattern Recognition — 50 citations into it
• Representation & Self-Supervised Learning — 32 citations into it
• Model Architectures — 24 citations into it
• Natural Language Processing — 19 citations into it
• Control and Systems Engineering — 18 citations into it
• Probabilistic & Bayesian Methods — 18 citations into it; #408 of 1,525 by trust here, #441 by citations
• Signal Processing — 11 citations into it; #5,558 of 7,572 by trust here, #5,570 by citations

Whom Markus Svensén trusts overall

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

1. Geoffrey E. Hinton
2. Michael I. Jordan
3. Alex Krizhevsky
4. Yann LeCun
5. Alex Graves
6. Yoshua Bengio
7. Radford M. Neal
8. David John Cameron MacKay
9. Ilya Sutskever
10. Diederik P. Kingma

Who trusts Markus Svensén overall

1. Neil D. Lawrence
2. Yuhui Du
3. Benigno Uria
4. Balázs Kégl
5. Lawrence K. Saul
6. John M. Winn
7. Isobel Claire Gormley
8. Matthias Hein
9. Vince D. Calhoun
10. L. V. D. van der Maaten

Papers from Markus Svensén's perspective

Ordered by how strongly the researchers Markus Svensén 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Potential, challenges and future directions for deep learning in prognostics and health management applications (2020) — 488 citations
• Bayesian Hierarchical Mixtures of Experts (2002) — 172 citations
• ICA of fMRI Group Study Data (2002) — 143 citations
• GTM: The Generative Topographic Mapping (1998)