Julio Hurtado
Artificial Intelligence · 2021
A researcher in the Science corpus: 2 papers; trusts 156 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 88 citations into it; #28,557 of 35,345 by trust here, #29,412 by citations
- Computer Vision and Pattern Recognition — 53 citations into it; #17,682 of 21,575 by trust here, #18,782 by citations
- Meta & Continual Learning — 47 citations into it; #879 of 1,041 by trust here, #963 by citations
- Reinforcement Learning — 30 citations into it
- Representation & Self-Supervised Learning — 26 citations into it
- Optimization & Training — 7 citations into it
- Efficiency & Compression — 6 citations into it
Whom Julio Hurtado trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Marc'Aurelio Ranzato
- Arslan Chaudhry
- Yoshua Bengio
- Diederik P. Kingma
- Kaiming He
- Yann LeCun
- Oriol Vinyals
- Marcus Rohrbach
Who trusts Julio Hurtado overall
- Jorge Armando Mendez Mendez
- Haoxuan Qu
- Grzegorz Rype's'c
- Eric Eaton
- Hossein Rahmani
- Liyuan Wang
- Sebastian Cygert
Papers from Julio Hurtado's perspective
Ordered by how strongly the researchers Julio Hurtado trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Optimizing Reusable Knowledge for Continual Learning via Metalearning (2021) — 46 citations
- Sequoia: A Software Framework to Unify Continual Learning Research (2021) — 25 citations