David P. Reichert

Artificial Intelligence · 2013–2019

A researcher in the Science corpus: 9 papers; trusts 164 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 181 citations into it; #11,867 of 35,345 by trust here, #3,183 by citations
• Reinforcement Learning — 130 citations into it; #1,053 of 2,242 by trust here, #288 by citations
• Computer Vision and Pattern Recognition — 110 citations into it
• Model Architectures — 35 citations into it
• Cognitive Neuroscience — 27 citations into it; #4,729 of 10,852 by trust here, #8,363 by citations
• Generative Models — 22 citations into it
• Representation & Self-Supervised Learning — 22 citations into it
• Optimization & Training — 18 citations into it

Whom David P. Reichert trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Ruslan Salakhutdinov
4. Tijmen Tieleman
5. Ian J. Goodfellow
6. Paul Smolensky
7. Yann LeCun
8. Diederik P. Kingma
9. Alex Krizhevsky
10. Richard S. Sutton

Who trusts David P. Reichert overall

1. Klaus Greff
2. J. Urgen Schmidhuber
3. Sjoerd van Steenkiste
4. Benyou Wang
5. David R Ha
6. Sergey Levine
7. Danijar Hafner
8. Yoshua Bengio
9. Yuxi Li
10. Anirudh Goyal

Papers from David P. Reichert's perspective

Ordered by how strongly the researchers David P. Reichert trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Neural scene representation and rendering (2018) — 674 citations
• Imagination-Augmented Agents for Deep Reinforcement Learning (2017) — 599 citations
• The Predictron: End-To-End Learning and Planning (2016) — 310 citations
• Learning and Querying Fast Generative Models for Reinforcement Learning (2018) — 135 citations
• Learning model-based planning from scratch (2017) — 102 citations
• Relational Deep Reinforcement Learning (2018) — 245 citations
• Automated curricula through setter-solver interactions (2019) — 60 citations
• Neuronal Synchrony in Complex-Valued Deep Networks (2013) — 121 citations
• Deep reinforcement learning with relational inductive biases (2018) — 231 citations