Matt Barnes

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 16 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 59 citations into it; #33,846 of 35,345 by trust here, #22,463 by citations
• Reinforcement Learning — 49 citations into it; #2,155 of 2,242 by trust here, #1,895 by citations
• Control and Systems Engineering — 26 citations into it; #7,870 of 8,257 by trust here, #3,865 by citations
• Computer Vision and Pattern Recognition — 22 citations into it; #20,685 of 21,575 by trust here, #14,822 by citations
• Management Science and Operations Research — 11 citations into it
• Statistical and Nonlinear Physics — 7 citations into it
• Optimization & Training — 6 citations into it

Whom Matt Barnes trusts overall

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

1. Stéphane Ross
2. Pieter Abbeel
3. Sergey Levine
4. J. Bagnell
5. Jonathan Ho
6. Geoffrey E. Hinton
7. Diederik P. Kingma
8. Andrew Y. Ng
9. Yoshua Bengio
10. Richard S. Sutton

Who trusts Matt Barnes overall

1. Ofir Nachum
2. Ilya Kostrikov

Papers from Matt Barnes's perspective

Ordered by how strongly the researchers Matt Barnes 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. GENERATIVE ADVERSARIAL NETS
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Learning from Interventions: Human-robot interaction as both explicit and implicit feedback (2020) — 84 citations
• Imitation Learning as f-Divergence Minimization (2019) — 193 citations
• Mo' States Mo' Problems: Emergency Stop Mechanisms from Observation (2019) — 7 citations