James Jordon

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 7 papers; trusts 169 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 75 citations into it; #13,045 of 35,345 by trust here, #17,110 by citations
• Statistics and Probability — 55 citations into it; #7,682 of 12,932 by trust here, #5,661 by citations
• Computer Vision and Pattern Recognition — 34 citations into it; #9,897 of 21,575 by trust here, #15,955 by citations
• Representation & Self-Supervised Learning — 32 citations into it
• Causal Inference & Discovery — 27 citations into it; #125 of 364 by trust here, #144 by citations
• Optimization & Training — 12 citations into it
• Management Science and Operations Research — 11 citations into it
• Reinforcement Learning — 10 citations into it

Whom James Jordon trusts overall

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

1. James M. Robins
2. Diederik P. Kingma
3. Yoshua Bengio
4. Ahmed M. Alaa
5. Geoffrey E. Hinton
6. Donald B. Rubin
7. Individualized Treat
8. Pascal Vincent
9. Alex Krizhevsky
10. Mihaela van der Schaar

Who trusts James Jordon overall

1. Ioana Bica
2. Mihaela van der Schaar
3. Samuel A. Assefa
4. Ahmed M. Alaa
5. Jinsung Yoon
6. Bryan Lim
7. Reihaneh Torkzadehmahani
8. Ying-Cheng Lu
9. Liuyi Yao
10. Vadim Borisov

Papers from James Jordon's perspective

Ordered by how strongly the researchers James Jordon 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
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

• GAIN: Missing Data Imputation using Generative Adversarial Nets (2018) — 1,410 citations
• Deep-Treat: Learning Optimal Personalized Treatments From Observational Data Using Neural Networks (2018) — 76 citations
• Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations (2020) — 241 citations
• Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks (2020) — 128 citations
• VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain (2020) — 346 citations
• PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees (2018) — 850 citations
• GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets (2018) — 473 citations