Stephen James

Artificial Intelligence · 2016–2022

A researcher in the Science corpus: 8 papers; trusts 496 researchers and is trusted by 51 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 159 citations into it; #4,846 of 35,345 by trust here, #5,002 by citations
• Computer Vision and Pattern Recognition — 150 citations into it; #5,486 of 21,575 by trust here, #5,333 by citations
• Reinforcement Learning — 111 citations into it; #564 of 2,242 by trust here, #723 by citations
• Control and Systems Engineering — 93 citations into it; #509 of 8,257 by trust here, #242 by citations
• Robotics & Control — 48 citations into it; #90 of 609 by trust here, #89 by citations
• Representation & Self-Supervised Learning — 46 citations into it
• Aerospace Engineering — 36 citations into it
• Biomedical Engineering — 26 citations into it

Whom Stephen James trusts overall

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

1. Diederik P. Kingma
2. Sergey Levine
3. Pieter Abbeel
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Chelsea Finn
7. Ilya Sutskever
8. Yoshua Bengio
9. Kaiming He
10. Jimmy Ba

Who trusts Stephen James overall

1. Chelsea Finn
2. Sergey Levine
3. Andrew J. Davison
4. Julian Ibarz
5. Mohit Shridhar
6. Suraj Nair
7. Michael Crawshaw
8. Jonathan Tremblay
9. Eric Jang
10. Mohi Khansari

Papers from Stephen James's perspective

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

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

Their papers

• Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task (2017) — 291 citations
• RLBench: The Robot Learning Benchmark & Learning Environment (2019) — 985 citations
• Sim-To-Real via Sim-To-Sim: Data-Efficient Robotic Grasping via Randomized-To-Canonical Adaptation Networks (2018) — 508 citations
• Task-Embedded Control Networks for Few-Shot Imitation Learning (2018) — 150 citations
• Sim-to-Real Reinforcement Learning for Deformable Object Manipulation (2018) — 424 citations
• 3D Simulation for Robot Arm Control with Deep Q-Learning (2016) — 111 citations
• Real-World Robot Learning with Masked Visual Pre-training (2022) — 356 citations
• PyRep: Bringing V-REP to Deep Robot Learning (2019) — 130 citations