Brenden K. Petersen
Artificial Intelligence · 2018–2019
A researcher in the Science corpus: 2 papers; trusts 67 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 30 citations into it
- Reinforcement Learning — 21 citations into it
- Computer Vision and Pattern Recognition — 12 citations into it
- Model Architectures — 5 citations into it
- Optimization & Training — 5 citations into it
Whom Brenden K. Petersen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Barret Zoph
- Ronald J. Williams
- Quoc V. Le
- Aäron van den Oord
- Geoffrey E. Hinton
- Prajit Ramachandran
- Aviv Tamar
- Roger Fletcher
- John R. Koza
- Alex Krizhevsky
Who trusts Brenden K. Petersen overall
- Guillaume Lample
- William La Cava
- Chao Yu
- Jixiang Guo
- Hanchen Wang
- Marie-Anne Lachaux
- Patryk Orzechowski
- Jiming Liu
- Mingming Gong
Papers from Brenden K. Petersen's perspective
Ordered by how strongly the researchers Brenden K. Petersen trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients (2019) — 486 citations
- Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis (2018) — 27 citations