Rein Houthooft

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 8 papers; trusts 173 researchers and is trusted by 1,497 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 197 citations into it; #1,766 of 35,345 by trust here, #2,311 by citations
• Reinforcement Learning — 191 citations into it; #172 of 2,242 by trust here, #140 by citations
• Computer Vision and Pattern Recognition — 55 citations into it; #1,111 of 21,575 by trust here, #917 by citations
• Management Science and Operations Research — 43 citations into it
• Optimization & Training — 37 citations into it
• Representation & Self-Supervised Learning — 29 citations into it; #602 of 3,145 by trust here, #172 by citations
• Computational Theory and Mathematics — 28 citations into it
• Cognitive Neuroscience — 23 citations into it

Whom Rein Houthooft trusts overall

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

1. J. Urgen Schmidhuber
2. Ronald J. Williams
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. John Schulman
6. Richard S. Sutton
7. Jan Peters
8. Yoshua Bengio
9. Sergey Levine
10. Alex Graves

Who trusts Rein Houthooft overall

1. Sergey Levine
2. Pieter Abbeel
3. Abhishek Gupta
4. Yoshua Bengio
5. Chelsea Finn
6. Deepak Pathak
7. Trevor Darrell
8. Ruslan Salakhutdinov
9. Ofir Nachum
10. Abhinav Kumar Gupta

Papers from Rein Houthooft's perspective

Ordered by how strongly the researchers Rein Houthooft 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
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

• InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets (2016) — 4,557 citations
• Benchmarking Deep Reinforcement Learning for Continuous Control (2016) — 1,830 citations
• Parameter Space Noise for Exploration (2017) — 689 citations
• Evolved Policy Gradients (2018) — 238 citations
• Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks (2016) — 80 citations
• Some Considerations on Learning to Explore via Meta-Reinforcement Learning (2018) — 130 citations
• #Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning (2016) — 866 citations
• VIME: Variational Information Maximizing Exploration (2016) — 846 citations