S. M. Mousavi
Artificial Intelligence · Nature Communications · 2016–2022
A researcher in the Science corpus: 7 papers; trusts 61 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 62 citations into it; #13,439 of 35,345 by trust here, #20,836 by citations
- Geophysics — 61 citations into it; #340 of 1,958 by trust here, #640 by citations
- Computer Vision and Pattern Recognition — 34 citations into it; #15,673 of 21,575 by trust here, #19,766 by citations
- Ocean Engineering — 13 citations into it
- Model Architectures — 12 citations into it
- Natural Language Processing — 10 citations into it
- Optimization & Training — 9 citations into it
- Mechanical Engineering — 7 citations into it
Whom S. M. Mousavi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Zachary E. Ross
- Weiqiang Zhu
- Men‐Andrin Meier
- Gregory C. Beroza
- Diederik P. Kingma
- T. Perol
- Geoffrey E. Hinton
- Kaiming He
- Radford M. Neal
- Yoshua Bengio
Who trusts S. M. Mousavi overall
Papers from S. M. Mousavi's perspective
Ordered by how strongly the researchers S. M. Mousavi 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Deep-learning seismology (2022) — 440 citations
- Earthquake Phase Association Using a Bayesian Gaussian Mixture Model (2021) — 144 citations
- Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking (2020) — 973 citations
- STanford EArthquake Dataset (STEAD): A Global Data Set of Seismic Signals for AI (2019) — 368 citations
- Seismic Signal Denoising and Decomposition Using Deep Neural Networks (2018) — 406 citations
- Automatic noise-removal/signal-removal based on general cross-validation thresholding in synchrosqueezed domain and its application on earthquake data (2017) — 125 citations
- Hybrid Seismic Denoising Using Higher-Order Statistics and Improved Wavelet Block Thresholding (2016) — 186 citations