darch

darch: Package for deep architectures and Restricted-Bolzmann-Machines. The darch package is build on the basis of the code from G. E. Hinton and R. R. Salakhutdinov (available under Matlab Code for deep belief nets : last visit: 01.08.2013). This package is for generating neural networks with many layers (deep architectures) and train them with the method introduced by the publications ”A fast learning algorithm for deep belief nets” (G. E. Hinton, S. Osindero, Y. W. Teh) and ”Reducing the dimensionality of data with neural networks” (G. E. Hinton, R. R. Salakhutdinov). This method includes a pre training with the contrastive divergence method publishing by G.E Hinton (2002) and a fine tuning with common known training algorithms like backpropagation or conjugate gradient.


References in zbMATH (referenced in 306 articles )

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  1. Wu, Ling; Noels, Ludovic: Recurrent neural networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step (2022)
  2. Angeli, Andrea; Desmet, Wim; Naets, Frank: Deep learning for model order reduction of multibody systems to minimal coordinates (2021)
  3. Bhattacharya, Kaushik; Hosseini, Bamdad; Kovachki, Nikola B.; Stuart, Andrew M.: Model reduction and neural networks for parametric PDEs (2021)
  4. Chen, Yuan; Zeng, Donglin; Wang, Yuanjia: Learning individualized treatment rules for multiple-domain latent outcomes (2021)
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  6. Dong, Hao; Nie, Yufeng; Cui, Junzhi; Kou, Wenbo; Zou, Minqiang; Han, Junyan; Guan, Xiaofei; Yang, Zihao: A wavelet-based learning approach assisted multiscale analysis for estimating the effective thermal conductivities of particulate composites (2021)
  7. Franz, Arthur; Antonenko, Oleksandr; Soletskyi, Roman: A theory of incremental compression (2021)
  8. Fu, Jinlong; Cui, Shaoqing; Cen, Song; Li, Chenfeng: Statistical characterization and reconstruction of heterogeneous microstructures using deep neural network (2021)
  9. Gao, Yu; Zhang, Kai: Machine learning based data retrieval for inverse scattering problems with incomplete data (2021)
  10. Ghods, Alireza; Cook, Diane J.: A survey of deep network techniques all classifiers can adopt (2021)
  11. Gunnarsson, Björn Rafn; vanden Broucke, Seppe; Baesens, Bart; Óskarsdóttir, María; Lemahieu, Wilfried: Deep learning for credit scoring: do or don’t? (2021)
  12. Gu, Shihao; Kelly, Bryan; Xiu, Dacheng: Autoencoder asset pricing models (2021)
  13. Hammadi, Youssef; Ryckelynck, David; El-Bakkali, Amin: Data-driven reduced bond graph for nonlinear multiphysics dynamic systems (2021)
  14. He, Chunmei; Wang, Shunmin; Kang, Hongyu; Zheng, Lanqing; Tan, Taifeng; Fan, Xianjun: Adversarial domain adaptation network for tumor image diagnosis (2021)
  15. He, Xiaolong; He, Qizhi; Chen, Jiun-Shyan: Deep autoencoders for physics-constrained data-driven nonlinear materials modeling (2021)
  16. Hu, Junying; Sun, Kai; Zhang, Hai: Enhancing performance of the back-propagation algorithm based on a novel regularization method of preserving inter-object-distance of data (2021)
  17. Isomura, Takuya; Toyoizumi, Taro: On the achievability of blind source separation for high-dimensional nonlinear source mixtures (2021)
  18. Jiang, Su; Durlofsky, Louis J.: Data-space inversion using a recurrent autoencoder for time-series parameterization (2021)
  19. Khoo, Yuehaw; Lu, Jianfeng; Ying, Lexing: Solving parametric PDE problems with artificial neural networks (2021)
  20. Kim, Cheolwoong; Lee, Jaewook; Yoo, Jeonghoon: Machine learning-combined topology optimization for functionary graded composite structure design (2021)

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