Felipe Petroski Such

Artificial Intelligence · 2017–2025

A researcher in the Science corpus: 11 papers; trusts 254 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 278 citations into it; #13,503 of 35,345 by trust here, #1,144 by citations
• Computer Vision and Pattern Recognition — 130 citations into it
• Reinforcement Learning — 91 citations into it; #1,954 of 2,242 by trust here, #1,174 by citations
• Representation & Self-Supervised Learning — 55 citations into it
• Optimization & Training — 41 citations into it
• Model Architectures — 37 citations into it
• Evaluation & Benchmarks — 32 citations into it
• Graph Learning — 23 citations into it

Whom Felipe Petroski Such trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Alex Krizhevsky
5. Kaiming He
6. Yann LeCun
7. Richard S. Sutton
8. Ilya Sutskever
9. J. Urgen Schmidhuber
10. Alex Graves

Who trusts Felipe Petroski Such overall

1. Jeff Clune
2. Shauharda Khadka
3. Steven Dalton
4. Adrien Ecoffet
5. Tal Ben-Nun
6. Niels Justesen
7. Karim Ahmed
8. Sheng-Chun Kao
9. Jiale Zhi
10. David R Ha

Papers from Felipe Petroski Such's perspective

Ordered by how strongly the researchers Felipe Petroski Such 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Evaluating Large Language Models Trained on Code (2021) — 10,663 citations
• An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution (2018) — 1,041 citations
• Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning (2017) — 759 citations
• Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents (2017) — 382 citations
• An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents (2018) — 55 citations
• Generalized Hidden Parameter MDPs Transferable Model-based RL in a Handful of Trials (2020) — 40 citations
• Text and Code Embeddings by Contrastive Pre-Training (2022) — 613 citations
• OpenAI GPT-5 System Card (2025) — 667 citations
• GPT-4o System Card (2024) — 4,622 citations
• GPT-4 Technical Report (2023) — 26,133 citations
• Robust Spatial Filtering With Graph Convolutional Neural Networks (2017) — 142 citations