Aleksandra Faust

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 8 papers; trusts 82 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 164 citations into it; #14,887 of 35,345 by trust here, #8,283 by citations
• Reinforcement Learning — 76 citations into it
• Computer Vision and Pattern Recognition — 68 citations into it; #12,427 of 21,575 by trust here, #7,499 by citations
• Control and Systems Engineering — 24 citations into it
• LLM Agents & Tool Use — 18 citations into it
• Optimization & Training — 18 citations into it
• Model Architectures — 17 citations into it
• Aerospace Engineering — 11 citations into it; #3,035 of 6,421 by trust here, #2,026 by citations

Whom Aleksandra Faust trusts overall

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

1. Richard S. Sutton
2. Geoffrey E. Hinton
3. Volodymyr Mnih
4. Alex Graves
5. Sergey Levine
6. Yoshua Bengio
7. Diederik P. Kingma
8. John Schulman
9. Andrew Y. Ng
10. Alex Krizhevsky

Who trusts Aleksandra Faust overall

1. Kai Zhu
2. Mohak Bhardwaj
3. J. Sánchez-Ibáñez
4. Tao Zhang
5. Ajay Kumar Sridhar
6. Hao-Tien Lewis Chiang
7. Dhruv Shah
8. Ayzaan Wahid
9. Izzeddin Gur
10. Bo Liu

Papers from Aleksandra Faust's perspective

Ordered by how strongly the researchers Aleksandra Faust 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
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

• PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-Based Planning (2017) — 332 citations
• Learning Navigation Behaviors End-to-End With AutoRL (2018) — 261 citations
• Learning to Navigate the Web (2018) — 78 citations
• Lyapunov-based Safe Policy Optimization for Continuous Control (2019) — 297 citations
• Understanding HTML with Large Language Models (2022) — 95 citations
• A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis (2023) — 391 citations
• Evolving Reinforcement Learning Algorithms (2021) — 84 citations
• Adversarial Environment Generation for Learning to Navigate the Web (2021) — 19 citations