Juan Mario Haut

Computer Vision and Pattern Recognition · 2017–2023

A researcher in the Science corpus: 5 papers; trusts 324 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 154 citations into it
• Media Technology — 151 citations into it; #1,461 of 3,621 by trust here, #828 by citations
• Artificial Intelligence — 101 citations into it
• Atmospheric Science — 86 citations into it; #2,941 of 4,804 by trust here, #1,659 by citations
• Model Architectures — 55 citations into it
• Optimization & Training — 33 citations into it
• Analytical Chemistry — 32 citations into it
• Ecology — 26 citations into it

Whom Juan Mario Haut trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Yann LeCun
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Kaiming He
7. Sergey Ioffe
8. Ilya Sutskever
9. Ruslan Salakhutdinov
10. Karen Simonyan

Who trusts Juan Mario Haut overall

1. Swalpa Kumar Roy
2. Mercedes Eugenia Paoletti
3. Muhammad Ahmad
4. Guangrui Zhao
5. Suvojit Manna
6. Shutao Li
7. Xin He

Papers from Juan Mario Haut's perspective

Ordered by how strongly the researchers Juan Mario Haut 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• Capsule Networks for Hyperspectral Image Classification (2019) — 311 citations
• Spectral–Spatial Morphological Attention Transformer for Hyperspectral Image Classification (2023) — 371 citations
• Deep Pyramidal Residual Networks for Spectral–Spatial Hyperspectral Image Classification (2019) — 428 citations
• Deep learning classifiers for hyperspectral imaging: A review (2019) — 930 citations
• A new deep convolutional neural network for fast hyperspectral image classification (2017) — 563 citations