Florian Shkurti

Artificial Intelligence · with Dudek · 2017–2020

A researcher in the Science corpus: 4 papers; trusts 117 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 80 citations into it
• Reinforcement Learning — 65 citations into it
• Computer Vision and Pattern Recognition — 30 citations into it
• Management Science and Operations Research — 20 citations into it
• Control and Systems Engineering — 17 citations into it
• Probabilistic & Bayesian Methods — 10 citations into it
• Optimization & Training — 9 citations into it
• Generative Models — 8 citations into it

Whom Florian Shkurti trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Richard S. Sutton
6. Yann LeCun
7. Sergey Levine
8. Ian J. Goodfellow
9. Ilya Sutskever
10. Andrew Y. Ng

Who trusts Florian Shkurti overall

1. Li Chen

Papers from Florian Shkurti's perspective

Ordered by how strongly the researchers Florian Shkurti 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. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
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

• Benchmark Environments for Multitask Learning in Continuous Domains (2017) — 42 citations
• Continual Model-Based Reinforcement Learning with Hypernetworks (2020) — 64 citations
• Shaping Rewards for Reinforcement Learning with Imperfect Demonstrations using Generative Models (2020) — 26 citations
• Generating Adversarial Driving Scenarios in High-Fidelity Simulators (2019) — 149 citations