Wittawat Jitkrittum
Artificial Intelligence · 2012–2016
A researcher in the Science corpus: 4 papers; trusts 48 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 49 citations into it
- Statistics and Probability — 37 citations into it; #11,168 of 12,932 by trust here, #5,294 by citations
- Statistical Learning Theory — 30 citations into it
- Probabilistic & Bayesian Methods — 18 citations into it
- Computer Vision and Pattern Recognition — 15 citations into it
- Computational Mechanics — 10 citations into it
- Optimization & Training — 8 citations into it
Whom Wittawat Jitkrittum trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Arthur Gretton
- Alex Smola
- Christopher K. I. Williams
- Bernhard Schölkopf
- Ingo Steinwart
- Bharath K. Sriperumbudur
- Kenji Fukumizu
- Le Song
- Francis R. Bach
- Geoffrey E. Hinton
Who trusts Wittawat Jitkrittum overall
- David Lopez-Paz
- Krikamol Muandet
- Danica J. Sutherland
- Gwonsoo Che
- Balaji Lakshminarayanan
- Feng Liu
- Maxime Oquab
- Kenji Fukumizu
- Leonard Hasenclever
- Hongseok Yang
Papers from Wittawat Jitkrittum's perspective
Ordered by how strongly the researchers Wittawat Jitkrittum 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
- Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages (2015) — 33 citations
- High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso (2012) — 344 citations
- K2-ABC: Approximate Bayesian Computation with Infinite Dimensional Summary Statistics via Kernel Embeddings (2015) — 10 citations
- Interpretable Distribution Features with Maximum Testing Power (2016) — 148 citations