Paul Barde

Reinforcement Learning · 2019

A researcher in the Science corpus: 1 paper; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 23 citations into it
• Artificial Intelligence — 22 citations into it

Whom Paul Barde trusts overall

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

1. Michael L. Littman
2. Jakob N. Foerster
3. Richard S. Sutton
4. Igor Mordatch
5. Geoffrey E. Hinton
6. Shariq Iqbal
7. Pieter Abbeel
8. Diederik P. Kingma
9. Yoshua Bengio
10. Tabish Rashid

Papers from Paul Barde's perspective

Ordered by how strongly the researchers Paul Barde 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. Long Short-Term Memory
4. Human-level control through deep reinforcement learning
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Continuous control with deep reinforcement learning (International Conference on Learning Representations)
9. ImageNet: A large-scale hierarchical image database
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning (2019) — 24 citations