B. G

Statistical Learning Theory · 2000

A researcher in the Science corpus: 1 paper; trusts 5 researchers and is trusted by 63 researchers here, as inferred from the citation graph.

Subfields

• Statistical Learning Theory — 5 citations into it

Whom B. G trusts overall

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

1. Vladimir Naumovich Vapni
2. R. Fisher
3. Donald F. Specht
4. Bernhard Schölkopf
5. Vladimir Naumovich Vapnik
6. Alex Smola
7. C. Burges
8. K. Müller

Who trusts B. G overall

1. Thomas G. Dietterich
2. Han-Ming Wu
3. R. Strauss
4. Tony Van Gestel
5. Xiaofei He
6. Tae-Kyun Kim
7. Javier M. Moguerza
8. Shaohua Kevin Zhou
9. Mark D. Reid
10. Heesung Kwon

Papers from B. G's perspective

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

Their papers

• Generalized Discriminant Analysis Using a Kernel Approach (2000) — 1,823 citations