Michael Luck

Artificial Intelligence · with Gavenski · 2024

A researcher in the Science corpus: 2 papers; trusts 78 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 80 citations into it
• Reinforcement Learning — 67 citations into it
• Computer Vision and Pattern Recognition — 52 citations into it
• Control and Systems Engineering — 38 citations into it
• Robotics & Control — 15 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Statistical and Nonlinear Physics — 6 citations into it

Whom Michael Luck trusts overall

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

1. Faraz Torabi
2. Geoffrey E. Hinton
3. Pieter Abbeel
4. Sergey Levine
5. Richard S. Sutton
6. Jonathan Ho
7. Diederik P. Kingma
8. Garrett Warnell
9. Yoshua Bengio
10. Alex Krizhevsky

Papers from Michael Luck's perspective

Ordered by how strongly the researchers Michael Luck 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. GENERATIVE ADVERSARIAL NETS
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Survey of Imitation Learning Methods, Environments and Metrics (2024) — 133 citations
• Imitation Learning: A Survey of Learning Methods, Environments and Metrics (2024) — 535 citations