Ron Dorfman

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 2 papers; trusts 100 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 47 citations into it; #30,231 of 35,345 by trust here, #34,213 by citations
• Reinforcement Learning — 35 citations into it
• Management Science and Operations Research — 9 citations into it
• Control and Systems Engineering — 8 citations into it
• Cognitive Neuroscience — 6 citations into it
• Computational Theory and Mathematics — 6 citations into it
• Meta & Continual Learning — 6 citations into it

Whom Ron Dorfman trusts overall

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

1. Sergey Levine
2. Pieter Abbeel
3. Chelsea Finn
4. Richard S. Sutton
5. Geoffrey E. Hinton
6. E. Todorov
7. Volodymyr Mnih
8. Aurick Zhou
9. Tuomas Haarnoja
10. Diederik P. Kingma

Who trusts Ron Dorfman overall

1. Glen Berseth
2. Zhiwei Zhang

Papers from Ron Dorfman's perspective

Ordered by how strongly the researchers Ron Dorfman 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Offline Meta Reinforcement Learning (2020) — 21 citations
• Offline Meta Reinforcement Learning - Identifiability Challenges and Effective Data Collection Strategies (2021) — 74 citations