Jamal Toutouh

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 6 papers; trusts 57 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 51 citations into it; #18,099 of 35,345 by trust here, #28,481 by citations
• Computer Vision and Pattern Recognition — 25 citations into it; #12,252 of 21,575 by trust here, #19,310 by citations
• Model Architectures — 23 citations into it
• Optimization & Training — 17 citations into it
• Neural Architecture Search & AutoML — 12 citations into it
• Management Science and Operations Research — 10 citations into it
• Electrical and Electronic Engineering — 9 citations into it
• Large Language Models — 6 citations into it

Whom Jamal Toutouh trusts overall

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

1. Andrés Camero
2. Diederik P. Kingma
3. Yoshua Bengio
4. Thomas Bäck
5. Geoffrey E. Hinton
6. Yann LeCun
7. Enrique Alba
8. Jimmy Ba
9. Risto Miikkulainen
10. Ilya Sutskever

Who trusts Jamal Toutouh overall

1. Andrés Camero
2. Enrique Alba
3. Javier Ferrer
4. Yuqiao Liu
5. Thomas Bäck

Papers from Jamal Toutouh's perspective

Ordered by how strongly the researchers Jamal Toutouh 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• A Specialized Evolutionary Strategy Using Mean Absolute Error Random Sampling to Design Recurrent Neural Networks (2019) — 16 citations
• Waste generation prediction under uncertainty in smart cities through deep neuroevolution (2019) — 14 citations
• Random error sampling-based recurrent neural network architecture optimization (2019) — 25 citations
• Evolutionary Deep Learning for Car Park Occupancy Prediction in Smart Cities (2018) — 102 citations
• Low-Cost Recurrent Neural Network Expected Performance Evaluation (2018) — 20 citations
• DLOPT: Deep Learning Optimization Library (2018) — 9 citations