PyTorch python package: Tensors and Dynamic neural networks in Python with strong GPU acceleration. PyTorch is a deep learning framework that puts Python first.

References in zbMATH (referenced in 170 articles )

Showing results 41 to 60 of 170.
Sorted by year (citations)

previous 1 2 3 4 5 ... 7 8 9 next

  1. Fernando Pérez-García, Rachel Sparks, Sebastien Ourselin: TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning (2020) arXiv
  2. Frank Mancolo: Eisen: a python package for solid deep learning (2020) arXiv
  3. Frazier-Logue, Noah; Hanson, Stephen José: The stochastic delta rule: faster and more accurate deep learning through adaptive weight noise (2020)
  4. Geneva, Nicholas; Zabaras, Nicholas: Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks (2020)
  5. Glazkova, A. V.: Topical classification of text fragments accounting for their nearest context (2020)
  6. Grinchuk, O. V.; Tsurkov, V. I.: Training a multimodal neural network to determine the authenticity of images (2020)
  7. Guo, Jian; He, He; He, Tong; Lausen, Leonard; Li, Mu; Lin, Haibin; Shi, Xingjian; Wang, Chenguang; Xie, Junyuan; Zha, Sheng; Zhang, Aston; Zhang, Hang; Zhang, Zhi; Zhang, Zhongyue; Zheng, Shuai; Zhu, Yi: GluonCV and GluonNLP: deep learning in computer vision and natural language processing (2020)
  8. Het Shah, Avishree Khare, Neelay Shah, Khizir Siddiqui: KD-Lib: A PyTorch library for Knowledge Distillation, Pruning and Quantization (2020) arXiv
  9. Iwata, Tomoharu; Toyoda, Machiko; Tora, Shotaro; Ueda, Naonori: Anomaly detection with inexact labels (2020)
  10. Jaap Jumelet: diagNNose: A Library for Neural Activation Analysis (2020) arXiv
  11. Jean Bégaint, Fabien Racapé, Simon Feltman, Akshay Pushparaja: CompressAI: a PyTorch library and evaluation platform for end-to-end compression research (2020) arXiv
  12. Jo, Hyeontae; Son, Hwijae; Hwang, Hyung Ju; Kim, Eun Heui: Deep neural network approach to forward-inverse problems (2020)
  13. Joshua G. Albert: JAXNS: a high-performance nested sampling package based on JAX (2020) arXiv
  14. Kalainathan, Diviyan; Goudet, Olivier; Dutta, Ritik: Causal discovery toolbox: uncovering causal relationships in Python (2020)
  15. Karumuri, Sharmila; Tripathy, Rohit; Bilionis, Ilias; Panchal, Jitesh: Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks (2020)
  16. Katrutsa, Alexandr; Daulbaev, Talgat; Oseledets, Ivan: Black-box learning of multigrid parameters (2020)
  17. Kazemi, Seyed Mehran; Goel, Rishab; Jain, Kshitij; Kobyzev, Ivan; Sethi, Akshay; Forsyth, Peter; Poupart, Pascal: Representation learning for dynamic graphs: a survey (2020)
  18. Kissas, Georgios; Yang, Yibo; Hwuang, Eileen; Witschey, Walter R.; Detre, John A.; Perdikaris, Paris: Machine learning in cardiovascular flows modeling: predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks (2020)
  19. Kossaifi, Jean; Lipton, Zachary C.; Kolbeinsson, Arinbjorn; Khanna, Aran; Furlanello, Tommaso; Anandkumar, Anima: Tensor regression networks (2020)
  20. Lingxiao He, Xingyu Liao, Wu Liu, Xinchen Liu, Peng Cheng, Tao Mei: FastReID: A Pytorch Toolbox for General Instance Re-identification (2020) arXiv

previous 1 2 3 4 5 ... 7 8 9 next