References in zbMATH (referenced in 98 articles )

Showing results 1 to 20 of 98.
Sorted by year (citations)

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  1. Alexander M. Rush: Torch-Struct: Deep Structured Prediction Library (2020) arXiv
  2. Alvaro Tejero-Canteroe; Jan Boeltse; Michael Deistlere; Jan-Matthis Lueckmanne; Conor Durkane; Pedro J. Gonçalves; David S. Greenberg; Jakob H. Macke: sbi: A toolkit for simulation-based inference (2020) not zbMATH
  3. Benedek Rozemberczki, Oliver Kiss, Rik Sarkar: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (2020) arXiv
  4. Benyi Hu, Ren-Jie Song, Xiu-Shen Wei, Yazhou Yao, Xian-Sheng Hua, Yuehu Liu: PyRetri: A PyTorch-based Library for Unsupervised Image Retrieval by Deep Convolutional Neural Networks (2020) arXiv
  5. Berahas, Albert S.; Takáč, Martin: A robust multi-batch L-BFGS method for machine learning (2020)
  6. Bertocchi, Carla; Chouzenoux, Emilie; Corbineau, Marie-Caroline; Pesquet, Jean-Christophe; Prato, Marco: Deep unfolding of a proximal interior point method for image restoration (2020)
  7. Chaoyang He, Songze Li, Jinhyun So, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Li Shen, Peilin Zhao, Yan Kang, Yang Liu, Ramesh Raskar, Qiang Yang, Murali Annavaram, Salman Avestimehr: FedML: A Research Library and Benchmark for Federated Machine Learning (2020) arXiv
  8. Cui, Ying; He, Ziyu; Pang, Jong-Shi: Multicomposite nonconvex optimization for training deep neural networks (2020)
  9. Daniel Deutsch, Dan Roth: SacreROUGE: An Open-Source Library for Using and Developing Summarization Evaluation Metrics (2020) arXiv
  10. Davis, Damek; Drusvyatskiy, Dmitriy; Kakade, Sham; Lee, Jason D.: Stochastic subgradient method converges on tame functions (2020)
  11. Dillon Niederhut: niacin: A Python package for text data enrichment (2020) not zbMATH
  12. Drori, Iddo: Deep variational inference (2020)
  13. Fangzhou Xie: Pruned Wasserstein Index Generation Model and wigpy Package (2020) arXiv
  14. Fan Mo, Ali Shahin Shamsabadi, Kleomenis Katevas, Soteris Demetriou, Ilias Leontiadis, Andrea Cavallaro, Hamed Haddadi: DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environments (2020) arXiv
  15. Feiyu Chen; David Sondak; Pavlos Protopapas; Marios Mattheakis; Shuheng Liu; Devansh Agarwal; Marco Di Giovanni: NeuroDiffEq: A Python package for solving differential equations with neural networks (2020) not zbMATH
  16. 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
  17. Frank Mancolo: Eisen: a python package for solid deep learning (2020) arXiv
  18. Katrutsa, Alexandr; Daulbaev, Talgat; Oseledets, Ivan: Black-box learning of multigrid parameters (2020)
  19. 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)
  20. Lingxiao He, Xingyu Liao, Wu Liu, Xinchen Liu, Peng Cheng, Tao Mei: FastReID: A Pytorch Toolbox for General Instance Re-identification (2020) arXiv

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