Mohammad Amin Nabian

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 3 papers; trusts 42 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 47 citations into it
• Statistical and Nonlinear Physics — 34 citations into it; #4,670 of 10,127 by trust here, #4,820 by citations
• Computer Vision and Pattern Recognition — 32 citations into it
• Optimization & Training — 20 citations into it
• Computational Mechanics — 19 citations into it; #4,954 of 10,001 by trust here, #6,041 by citations
• Electrical and Electronic Engineering — 13 citations into it
• AI for Physical Sciences — 11 citations into it
• Statistics and Probability — 10 citations into it; #6,700 of 12,932 by trust here, #6,853 by citations

Whom Mohammad Amin Nabian trusts overall

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

1. Dongbin Xiu
2. Pavel B. Bochev
3. L. Eon Bottou
4. Sebastian Ruder
5. Maizar Raissi
6. Max D. Gunzburger
7. Matthew D. Zeiler
8. Geoffrey E. Hinton
9. Ilya Sutskever
10. Kaiming He

Who trusts Mohammad Amin Nabian overall

1. Jakub Kudela
2. Yaohua Zang
3. R. Matousek
4. Gang Bao
5. Lu Lu
6. Han Gao
7. Hongwei Guo
8. Xiaojing Ye

Papers from Mohammad Amin Nabian's perspective

Ordered by how strongly the researchers Mohammad Amin Nabian 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• A Deep Neural Network Surrogate for High-Dimensional Random Partial Differential Equations (2018) — 109 citations
• Geometry-Informed Neural Operator for Large-Scale 3D PDEs (2023) — 378 citations
• Efficient training of physics‐informed neural networks via importance sampling (2021) — 366 citations