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 542 articles )

Showing results 1 to 20 of 542.
Sorted by year (citations)

1 2 3 ... 26 27 28 next

  1. Aricioğlu, Burak; Uzun, Süleyman; Kaçar, Sezgin: Deep learning based classification of time series of Chen and Rössler chaotic systems over their graphic images (2022)
  2. Avazov, Kuldoshbay; Abdusalomov, Akmalbek; Mukhiddinov, Mukhriddin; Baratov, Nodirbek; Makhmudov, Fazliddin; Cho, Young Im: An improvement for the automatic classification method for ultrasound images used on CNN (2022)
  3. Badreddine, Samy; d’Avila Garcez, Artur; Serafini, Luciano; Spranger, Michael: Logic tensor networks (2022)
  4. Basir, Shamsulhaq; Senocak, Inanc: Physics and equality constrained artificial neural networks: application to forward and inverse problems with multi-fidelity data fusion (2022)
  5. Bihlo, Alex; Popovych, Roman O.: Physics-informed neural networks for the shallow-water equations on the sphere (2022)
  6. Biswas, A.; Tian, J.; Ulusoy, S.: Error estimates for deep learning methods in fluid dynamics (2022)
  7. Boob, Digvijay; Dey, Santanu S.; Lan, Guanghui: Complexity of training ReLU neural network (2022)
  8. Bos, Thijs; Schmidt-Hieber, Johannes: Convergence rates of deep ReLU networks for multiclass classification (2022)
  9. Boute, Robert N.; Gijsbrechts, Joren; van Jaarsveld, Willem; Vanvuchelen, Nathalie: Deep reinforcement learning for inventory control: a roadmap (2022)
  10. Cai, Zhiqiang; Chen, Jingshuang; Liu, Min: Self-adaptive deep neural network: numerical approximation to functions and PDEs (2022)
  11. Caragea, Andrei; Lee, Dae Gwan; Maly, Johannes; Pfander, Götz; Voigtlaender, Felix: Quantitative approximation results for complex-valued neural networks (2022)
  12. Dang, Wei-Dong; Lv, Dong-Mei; Li, Ru-Mei; Rui, Lin-Ge; Yang, Zhuo-Yi; Ma, Chao; Gao, Zhong-Ke: Multilayer network-based CNN model for emotion recognition (2022)
  13. Dash, Tirtharaj; Srinivasan, Ashwin; Baskar, A.: Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment (2022)
  14. Daubechies, I.; DeVore, R.; Foucart, S.; Hanin, B.; Petrova, G.: Nonlinear approximation and (Deep) ReLU networks (2022)
  15. Duru, Cihat; Alemdar, Hande; Baran, Ozgur Ugras: A deep learning approach for the transonic flow field predictions around airfoils (2022)
  16. Fornasier, Massimo; Klock, Timo; Rauchensteiner, Michael: Robust and resource-efficient identification of two hidden layer neural networks (2022)
  17. Gao, Yihang; Ng, Michael K.: Wasserstein generative adversarial uncertainty quantification in physics-informed neural networks (2022)
  18. Gribonval, Rémi; Kutyniok, Gitta; Nielsen, Morten; Voigtlaender, Felix: Approximation spaces of deep neural networks (2022)
  19. Grohs, Philipp; Jentzen, Arnulf; Salimova, Diyora: Deep neural network approximations for solutions of PDEs based on Monte Carlo algorithms (2022)
  20. Guan, Yifei; Chattopadhyay, Ashesh; Subel, Adam; Hassanzadeh, Pedram: Stable \textitaposteriori LES of 2D turbulence using convolutional neural networks: backscattering analysis and generalization to higher (Re) via transfer learning (2022)

1 2 3 ... 26 27 28 next