Jason S. Hartford

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 4 papers; trusts 40 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it; #29,714 of 35,345 by trust here, #19,439 by citations
• Statistics and Probability — 24 citations into it
• Computer Vision and Pattern Recognition — 18 citations into it
• Optimization & Training — 16 citations into it
• Management Science and Operations Research — 13 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Causal Inference & Discovery — 11 citations into it; #253 of 364 by trust here, #191 by citations
• Graph Learning — 10 citations into it

Whom Jason S. Hartford trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Radford M. Neal
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Xiaohong Chen
5. Yann LeCun
6. L. Eon Bottou
7. Yoshua Bengio
8. Alex Krizhevsky
9. John C. Duchi
10. J. Urgen Schmidhuber

Who trusts Jason S. Hartford overall

1. Bryan Lim
2. Susan Athey
3. Haggai Maron
4. Uri Shalit
5. Hengyuan Hu
6. Guido W. Imbens
7. M. Usaid Awan
8. Ryan L. Murphy
9. Adam Lerer
10. Fredrik D. Johansson

Papers from Jason S. Hartford's perspective

Ordered by how strongly the researchers Jason S. Hartford trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Deep Models of Interactions Across Sets (2018) — 174 citations
• Counterfactual Prediction with Deep Instrumental Variables Networks (2016) — 48 citations
• Deep Learning for Predicting Human Strategic Behavior (2016) — 99 citations
• Deep IV: A Flexible Approach for Counterfactual Prediction (2017) — 362 citations