AlexNet

AlexNet is a convolutional neural network that is 8 layers deep. You can load a pretrained version of the network trained on more than a million images from the ImageNet database [1]. The pretrained network can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals. As a result, the network has learned rich feature representations for a wide range of images. The network has an image input size of 227-by-227. For more pretrained networks in MATLAB®, see Pretrained Deep Neural Networks.


References in zbMATH (referenced in 398 articles )

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  1. Adcock, Ben; Dexter, Nick: The gap between theory and practice in function approximation with deep neural networks (2021)
  2. Ao, Wenqi; Li, Wenbin; Qian, Jianliang: A data and knowledge driven approach for SPECT using convolutional neural networks and iterative algorithms (2021)
  3. Ayensa-Jiménez, Jacobo; Doweidar, Mohamed H.; Sanz-Herrera, Jose A.; Doblaré, Manuel: Prediction and identification of physical systems by means of physically-guided neural networks with meaningful internal layers (2021)
  4. Babu, G. Jogesh; Banks, David; Cho, Hyunsoon; Han, David; Sang, Hailin; Wang, Shouyi: A statistician teaches deep learning (2021)
  5. Celledoni, Elena; Ehrhardt, Matthias J.; Etmann, Christian; Owren, Brynjulf; Schönlieb, Carola-Bibiane; Sherry, Ferdia: Equivariant neural networks for inverse problems (2021)
  6. Chen, Tianbo; Sun, Ying; Li, Ta-Hsin: A semi-parametric estimation method for the quantile spectrum with an application to earthquake classification using convolutional neural network (2021)
  7. Chi, Heng; Zhang, Yuyu; Tang, Tsz Ling Elaine; Mirabella, Lucia; Dalloro, Livio; Song, Le; Paulino, Glaucio H.: Universal machine learning for topology optimization (2021)
  8. De Loera, Jesús A.; Haddock, Jamie; Ma, Anna; Needell, Deanna: Data-driven algorithm selection and tuning in optimization and signal processing (2021)
  9. Effland, Alexander; Kobler, Erich; Pock, Thomas; Rajković, Marko; Rumpf, Martin: Image morphing in deep feature spaces: theory and applications (2021)
  10. Fan, Jianqing; Ma, Cong; Zhong, Yiqiao: A selective overview of deep learning (2021)
  11. Fresca, Stefania; Dede’, Luca; Manzoni, Andrea: A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs (2021)
  12. Gambella, Claudio; Ghaddar, Bissan; Naoum-Sawaya, Joe: Optimization problems for machine learning: a survey (2021)
  13. Gao, Yu; Zhang, Kai: Machine learning based data retrieval for inverse scattering problems with incomplete data (2021)
  14. Ghods, Alireza; Cook, Diane J.: A survey of deep network techniques all classifiers can adopt (2021)
  15. Gordon, Andrew S. (ed.); Miller, Rob (ed.); Morgenstern, Leora (ed.); Turán, György (ed.): Preface (2021)
  16. Guo, Rui; Zhou, Yong; Zhao, Jiaqi; Yao, Rui; Liu, Bing; Zhang, Xunhui: Unsupervised spatial-awareness attention-based and multi-scale domain adaption network for point cloud classification (2021)
  17. Guo, Zhenfei; Bai, Ruixiang; Lei, Zhenkun; Jiang, Hao; Liu, Da; Zou, Jianchao; Yan, Cheng: CPINet: parameter identification of path-dependent constitutive model with automatic denoising based on CNN-LSTM (2021)
  18. Haghighat, Ehsan; Bekar, Ali Can; Madenci, Erdogan; Juanes, Ruben: A nonlocal physics-informed deep learning framework using the peridynamic differential operator (2021)
  19. Haghighat, Ehsan; Juanes, Ruben: SciANN: a keras/tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks (2021)
  20. Hao, Jie; Zhu, William: Architecture self-attention mechanism: nonlinear optimization for neural architecture search (2021)

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