Jorge Armando Mendez Mendez

Artificial Intelligence · 2020–2022

A researcher in the Science corpus: 4 papers; trusts 868 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 226 citations into it; #13,125 of 35,345 by trust here, #18,186 by citations
• Computer Vision and Pattern Recognition — 109 citations into it
• Meta & Continual Learning — 104 citations into it; #406 of 1,041 by trust here, #711 by citations
• Reinforcement Learning — 76 citations into it; #2,016 of 2,242 by trust here, #1,973 by citations
• Representation & Self-Supervised Learning — 57 citations into it; #1,725 of 3,145 by trust here, #2,624 by citations
• Model Architectures — 33 citations into it
• Natural Language Processing — 25 citations into it
• Control and Systems Engineering — 21 citations into it

Whom Jorge Armando Mendez Mendez trusts overall

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

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

Who trusts Jorge Armando Mendez Mendez overall

1. Oleksiy Ostapenko
2. Eric Eaton
3. Haoxuan Qu
4. Samuel Kessler
5. Maciej Wolczyk
6. Ferran Alet
7. Pau Rodr'iguez L'opez
8. Fabrice Normandin
9. Harm van Seijen
10. Qingfeng Lan

Papers from Jorge Armando Mendez Mendez's perspective

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

Their papers

• Lifelong Learning of Compositional Structures (2020) — 52 citations
• Modular Lifelong Reinforcement Learning via Neural Composition (2022) — 52 citations
• How to Reuse and Compose Knowledge for a Lifetime of Tasks: A Survey on Continual Learning and Functional Composition (2022) — 38 citations
• Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting (2020) — 42 citations