Nicholas C. Landolfi

Reinforcement Learning · 2019

A researcher in the Science corpus: 1 paper; trusts 44 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 30 citations into it
• Artificial Intelligence — 25 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it
• Computational Theory and Mathematics — 5 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Nicholas C. Landolfi trusts overall

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

1. Richard S. Sutton
2. Ronald J. Williams
3. Sergey Levine
4. Chelsea Finn
5. Diederik P. Kingma
6. Pieter Abbeel
7. John Schulman
8. Geoffrey E. Hinton
9. J. Urgen Schmidhuber
10. Yan Duan

Papers from Nicholas C. Landolfi's perspective

Ordered by how strongly the researchers Nicholas C. Landolfi 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. 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

• A Model-based Approach for Sample-efficient Multi-task Reinforcement Learning (2019) — 19 citations