Maxwell Goldstein

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 3 papers; trusts 221 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 136 citations into it; #26,019 of 35,345 by trust here, #25,809 by citations
• Reinforcement Learning — 91 citations into it
• Computer Vision and Pattern Recognition — 56 citations into it
• Representation & Self-Supervised Learning — 27 citations into it
• Management Science and Operations Research — 19 citations into it
• Optimization & Training — 18 citations into it
• Computational Theory and Mathematics — 16 citations into it
• Evaluation & Benchmarks — 15 citations into it

Whom Maxwell Goldstein trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yann LeCun
4. Alex Krizhevsky
5. Yoshua Bengio
6. Richard S. Sutton
7. John Schulman
8. J. Urgen Schmidhuber
9. Kaiming He
10. Ilya Sutskever

Who trusts Maxwell Goldstein overall

1. Sae-Young Chung
2. Denis Yarats
3. Minqi Jiang
4. Rishabh Agarwal
5. Ilya Kostrikov

Papers from Maxwell Goldstein's perspective

Ordered by how strongly the researchers Maxwell Goldstein 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Automatic Data Augmentation for Generalization in Deep Reinforcement Learning (2020) — 120 citations
• Fast Adaptation to New Environments via Policy-Dynamics Value Functions (2020) — 24 citations
• Automatic Data Augmentation for Generalization in Reinforcement Learning (2021) — 139 citations