Justin K. Pugh

Artificial Intelligence · 2013–2016

A researcher in the Science corpus: 3 papers; trusts 22 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 33 citations into it; #12,083 of 35,345 by trust here, #15,542 by citations
• Electrical and Electronic Engineering — 10 citations into it
• Cognitive Neuroscience — 8 citations into it
• Computer Vision and Pattern Recognition — 8 citations into it
• Mechanical Engineering — 6 citations into it; #4,832 of 8,300 by trust here, #7,261 by citations
• Representation & Self-Supervised Learning — 6 citations into it
• Cellular and Molecular Neuroscience — 5 citations into it
• Sociology and Political Science — 5 citations into it

Whom Justin K. Pugh trusts overall

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

1. Kenneth O. Stanley
2. Joel Lehman
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Sebastian Risi
6. Andrea Soltoggio
7. Marc'Aurelio Ranzato
8. Risto Miikkulainen
9. Yann LeCun
10. Jean-Baptiste Mouret

Who trusts Justin K. Pugh overall

1. Kenneth O. Stanley
2. Lisa B. Soros
3. Rui Wang
4. Joel Lehman
5. Sébastien Forestier
6. Konstantinos Chatzilygeroudis
7. Aldo Pacchiano
8. Jeff Clune
9. Edoardo Conti
10. Jack Parker-Holder

Papers from Justin K. Pugh's perspective

Ordered by how strongly the researchers Justin K. Pugh 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. GENERATIVE ADVERSARIAL NETS

Their papers

• Quality Diversity: A New Frontier for Evolutionary Computation (2016) — 687 citations
• Evolving multimodal controllers with HyperNEAT (2013) — 25 citations
• Real-time Hebbian Learning from Autoencoder Features for Control Tasks (2014) — 5 citations