Ted Sandler
Artificial Intelligence · 2008–2018
A researcher in the Science corpus: 2 papers; trusts 54 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 17 citations into it
- Reinforcement Learning — 9 citations into it
- Statistical Learning Theory — 7 citations into it
- Management Science and Operations Research — 7 citations into it
- Statistics and Probability — 7 citations into it
- Representation & Self-Supervised Learning — 6 citations into it
- Computer Vision and Pattern Recognition — 5 citations into it
Whom Ted Sandler trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- David A. McAllester
- J. Langford
- Alex Krizhevsky
- Thorsten Joachims
- Adith Swaminathan
- Yevgeny Seldin
- Matthias W. Seeger
- Kaiming He
- Trevor J. Hastie
- Olivier Catoni
Who trusts Ted Sandler overall
Papers from Ted Sandler's perspective
Ordered by how strongly the researchers Ted Sandler trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- 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
- Bayesian Counterfactual Risk Minimization (2018) — 39 citations
- Regularized Learning with Networks of Features (2008) — 53 citations