B. Üstün

Artificial Intelligence · 2004–2007

A researcher in the Science corpus: 3 papers; trusts 7 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 7 citations into it; #28,679 of 35,345 by trust here, #31,361 by citations
• Control and Systems Engineering — 6 citations into it; #6,680 of 8,257 by trust here, #3,865 by citations
• Analytical Chemistry — 5 citations into it; #506 of 1,254 by trust here, #338 by citations
• Biophysics — 5 citations into it
• Statistical Learning Theory — 5 citations into it
• Scaling & Learning Theory — 5 citations into it

Whom B. Üstün trusts overall

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

1. Bernhard Schölkopf
2. Alex Smola
3. Roman Rosipal
4. Robert N. Feudale
5. Vladimir Naumovich Vapni
6. Yuhai Wu
7. Sebastian Mika
8. L. Trejo
9. H. Luetkepohl
10. Jason Aaron Edward Weston

Who trusts B. Üstün overall

1. Zerina Mašetić
2. Christoffer Axelsson
3. Igor Santos
4. Abdulhamit Subasi
5. Andrew K. Skidmore
6. Felix Brezo

Papers from B. Üstün's perspective

Ordered by how strongly the researchers B. Üstün 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Comparing support vector machines to PLS for spectral regression applications (2004) — 370 citations
• Visualisation and interpretation of Support Vector Regression models. (2007) — 164 citations
• Facilitating the application of Support Vector Regression by using a universal Pearson VII function based kernel (2006) — 324 citations