Wacha Bounliphone
Statistics and Probability · 2014–2015
A researcher in the Science corpus: 2 papers; trusts 13 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 21 citations into it; #10,941 of 12,932 by trust here, #7,165 by citations
- Artificial Intelligence — 17 citations into it; #26,337 of 35,345 by trust here, #25,040 by citations
- Probabilistic & Bayesian Methods — 10 citations into it
- Statistical Learning Theory — 9 citations into it
- Generative Models — 8 citations into it; #1,828 of 1,956 by trust here, #1,715 by citations
- Computer Vision and Pattern Recognition — 7 citations into it; #19,976 of 21,575 by trust here, #17,258 by citations
Whom Wacha Bounliphone trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Arthur Gretton
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Yann LeCun
- Ruslan Salakhutdinov
- W. Hoeffding
- Alex Krizhevsky
- Bernhard Schölkopf
- Max Welling
Who trusts Wacha Bounliphone overall
- Ali Borji
- David Lopez-Paz
- Krikamol Muandet
- Casey Meehan
- Cristóbal Esteban
- Mikolaj Binkowski
- Jake Snell
- Qiantong Xu
- Kenji Fukumizu
- Maxime Oquab
Papers from Wacha Bounliphone's perspective
Ordered by how strongly the researchers Wacha Bounliphone trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- GENERATIVE ADVERSARIAL NETS
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- A Test of Relative Similarity For Model Selection in Generative Models (2015) — 88 citations
- A low variance consistent test of relative dependency (2014) — 8 citations