Voot Tangkaratt
Artificial Intelligence · 2013–2018
A researcher in the Science corpus: 2 papers; trusts 27 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 35 citations into it; #22,967 of 35,345 by trust here, #15,004 by citations
- Probabilistic & Bayesian Methods — 23 citations into it; #1,161 of 1,525 by trust here, #781 by citations
- Optimization & Training — 18 citations into it; #1,902 of 2,601 by trust here, #1,468 by citations
- Reinforcement Learning — 18 citations into it
- Statistics and Probability — 12 citations into it
- Computer Vision and Pattern Recognition — 7 citations into it
- Computational Mechanics — 5 citations into it
Whom Voot Tangkaratt trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ronald J. Williams
- Richard S. Sutton
- Geoffrey E. Hinton
- Radford M. Neal
- Diederik P. Kingma
- Alex Graves
- Jan Peters
- Sham M. Kakade
- Yoshua Bengio
- Yann LeCun
Who trusts Voot Tangkaratt overall
Papers from Voot Tangkaratt's perspective
Ordered by how strongly the researchers Voot Tangkaratt 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)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam (2018) — 296 citations
- Model-Based Policy Gradients with Parameter-Based Exploration by Least-Squares Conditional Density Estimation (2013) — 26 citations