Ethan Knight
Reinforcement Learning · 2018–2019
A researcher in the Science corpus: 2 papers; trusts 90 researchers and is trusted by 11 researchers here, as inferred from the citation graph.
Subfields
- Reinforcement Learning — 46 citations into it; #1,135 of 2,242 by trust here, #1,864 by citations
- Artificial Intelligence — 35 citations into it; #14,378 of 35,345 by trust here, #23,731 by citations
- Optimization & Training — 13 citations into it
- Management Science and Operations Research — 11 citations into it
- Computational Theory and Mathematics — 5 citations into it
Whom Ethan Knight trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Longxin Lin
- Richard S. Sutton
- Shun-ichi Amari
- Diederik P. Kingma
- James Martens
- Sham M. Kakade
- Ben J. A. Kröse
- Yoshua Bengio
- G. Tesauro
- Geoffrey E. Hinton
Who trusts Ethan Knight overall
Papers from Ethan Knight's perspective
Ordered by how strongly the researchers Ethan Knight 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
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Towards Characterizing Divergence in Deep Q-Learning (2019) — 105 citations
- Natural Gradient Deep Q-learning (2018) — 10 citations