Jason Gauci

Artificial Intelligence · 2009–2018

A researcher in the Science corpus: 3 papers; trusts 27 researchers and is trusted by 54 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 49 citations into it; #3,529 of 35,345 by trust here, #6,378 by citations
• Reinforcement Learning — 28 citations into it
• Mechanical Engineering — 10 citations into it; #291 of 8,300 by trust here, #2,039 by citations
• Neural Architecture Search & AutoML — 6 citations into it; #68 of 283 by trust here, #215 by citations
• Cognitive Neuroscience — 5 citations into it; #1,582 of 10,852 by trust here, #6,723 by citations
• Electrical and Electronic Engineering — 5 citations into it; #745 of 16,539 by trust here, #4,502 by citations
• Model Architectures — 5 citations into it; #428 of 2,278 by trust here, #842 by citations

Whom Jason Gauci trusts overall

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

1. Kenneth O. Stanley
2. Risto Miikkulainen
3. Richard S. Sutton
4. Xin Yao
5. Randall D. Beer
6. Geoffrey E. Hinton
7. Faustino J. Gomez
8. Dario Floreano
9. Andrew Barto
10. Byoung-Tak Zhang

Who trusts Jason Gauci overall

1. David R Ha
2. Shimon Whiteson
3. Sebastian Risi
4. J. Urgen Schmidhuber
5. Kenneth O. Stanley
6. Phillip Verbancsics
7. Jeff Clune
8. Kristofor D. Carlson
9. Gregory Morse
10. Scott Fujimoto

Papers from Jason Gauci's perspective

Ordered by how strongly the researchers Jason Gauci 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. Reinforcement Learning: An Introduction
8. ImageNet: A large-scale hierarchical image database
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

• A Hypercube-Based Encoding for Evolving Large-Scale Neural Networks (2009) — 875 citations
• Horizon: Facebook's Open Source Applied Reinforcement Learning Platform (2018) — 147 citations
• Autonomous Evolution of Topographic Regularities in Artificial Neural Networks (2010) — 164 citations