Joaquin Quionero-Candela
Artificial Intelligence · 2009
A researcher in the Science corpus: 1 paper; trusts 29 researchers and is trusted by 21 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 28 citations into it; #8,735 of 35,345 by trust here, #3,957 by citations
- Statistics and Probability — 17 citations into it
- Statistical Learning Theory — 13 citations into it
- Probabilistic & Bayesian Methods — 9 citations into it
- Optimization & Training — 8 citations into it
- Representation & Self-Supervised Learning — 8 citations into it
- Computer Vision and Pattern Recognition — 7 citations into it
- Information Systems — 5 citations into it
Whom Joaquin Quionero-Candela trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Michael I. Jordan
- Bernhard Schölkopf
- Masashi Sugiyama
- Yuhai Wu
- Jonathan Baxter
- Bianca Zadrozny
- David Ruppert
- Lotfi A. Zadeh
- Hirotugu Akaike
- James J. Heckman
Who trusts Joaquin Quionero-Candela overall
- Masashi Sugiyama
- Mingxia Liu
- Xingchao Peng
- Mateo Rojas-Carulla
- Qiang Yang
- Mingsheng Long
- Bernhard Schölkopf
- Adarsh Subbaswamy
- Boqing Gong
- Jian Liang
Papers from Joaquin Quionero-Candela's perspective
Ordered by how strongly the researchers Joaquin Quionero-Candela 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
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Dataset Shift in Machine Learning (2009) — 2,271 citations