Kurtland Chua

Artificial Intelligence · 2018

A researcher in the Science corpus: 1 paper; trusts 43 researchers and is trusted by 56 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 30 citations into it; #3,969 of 35,345 by trust here, #7,606 by citations
• Reinforcement Learning — 25 citations into it; #422 of 2,242 by trust here, #711 by citations
• Control and Systems Engineering — 21 citations into it
• Probabilistic & Bayesian Methods — 12 citations into it; #422 of 1,525 by trust here, #333 by citations
• Computational Theory and Mathematics — 8 citations into it
• Optimization & Training — 6 citations into it

Whom Kurtland Chua trusts overall

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

1. Sergey Levine
2. Carl E. Rasmussen
3. Pieter Abbeel
4. Marc Peter Deisenroth
5. Geoffrey E. Hinton
6. Radford M. Neal
7. Diederik P. Kingma
8. Jan Peters
9. Longxin Lin
10. Chelsea Finn

Who trusts Kurtland Chua overall

1. Sergey Levine
2. Michael Janner
3. Pieter Abbeel
4. Ignasi Clavera
5. Danijar Hafner
6. Kevin Lu
7. Gabriel Dulac-Arnold
8. Tingwu Wang
9. Fredrik K. Gustafsson
10. Igor Mordatch

Papers from Kurtland Chua's perspective

Ordered by how strongly the researchers Kurtland Chua 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models (2018) — 1,543 citations