Daniel D. Lee

Artificial Intelligence · with Zhang · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 8 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 25 citations into it
• Computer Vision and Pattern Recognition — 11 citations into it
• Reinforcement Learning — 11 citations into it

Whom Daniel D. Lee trusts overall

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

1. Richard S. Sutton
2. Diederik P. Kingma
3. Alex Graves

Papers from Daniel D. Lee's perspective

Ordered by how strongly the researchers Daniel D. Lee trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Reinforcement Learning: An Introduction
3. Asynchronous Methods for Deep Reinforcement Learning
4. ImageNet classification with deep convolutional neural networks
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. Playing Atari with Deep Reinforcement Learning (arXiv.org)
7. Gradient-based learning applied to document recognition
8. Neural Turing Machines
9. End-to-End Training of Deep Visuomotor Policies
10. Fully convolutional networks for semantic segmentation

Their papers

• Memory Augmented Control Networks (2017) — 80 citations
• Learning Optimal Resource Allocations in Wireless Systems (2018) — 237 citations