Nathan Gavenski

Artificial Intelligence · 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 Nathan Gavenski 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. Jonathan Ho
6. Richard S. Sutton
7. Diederik P. Kingma
8. Garrett Warnell
9. Alex Krizhevsky
10. Yoshua Bengio

Who trusts Nathan Gavenski overall

1. Tim Pearce

Papers from Nathan Gavenski's perspective

Ordered by how strongly the researchers Nathan Gavenski 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. 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. GENERATIVE ADVERSARIAL NETS
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

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