Jonathan G. Richens

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 4 papers; trusts 16 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 65 citations into it
• Probabilistic & Bayesian Methods — 19 citations into it
• Causal Inference & Discovery — 16 citations into it
• Statistics and Probability — 13 citations into it
• Management Science and Operations Research — 9 citations into it
• Computational Theory and Mathematics — 5 citations into it
• History and Philosophy of Science — 5 citations into it

Whom Jonathan G. Richens trusts overall

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

1. Judea Pearl
2. David E. Heckerman
3. Ilya Shpitser
4. Quaid D. Morris
5. Daniel Nikolaev Nikovski
6. Alexander Balke
7. Ciarán M. Lee
8. Jan Peters
9. Irina Rish
10. Hannah L Semigran

Who trusts Jonathan G. Richens overall

1. Bernhard Schölkopf

Papers from Jonathan G. Richens's perspective

Ordered by how strongly the researchers Jonathan G. Richens 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 Large Scale Visual Recognition Challenge
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Improving the accuracy of medical diagnosis with causal machine learning (2020) — 557 citations
• Author Correction: Improving the accuracy of medical diagnosis with causal machine learning (2021) — 11 citations
• Learning medical triage from clinicians using Deep Q-Learning (2020) — 4 citations
• MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming (2019) — 19 citations