Caffe

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license.


References in zbMATH (referenced in 51 articles )

Showing results 1 to 20 of 51.
Sorted by year (citations)

1 2 3 next

  1. Ankit, Aayush; El Hajj, Izzat; Chalamalasetti, Sai Rahul; Agarwal, Sapan; Marinella, Matthew; Foltin, Martin; Strachan, John Paul; Milojicic, Dejan; Hwu, Wen-Mei; Roy, Kaushik: PANTHER: a programmable architecture for neural network training harnessing energy-efficient ReRAM (2020)
  2. 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)
  3. Kuwajima, Hiroshi; Yasuoka, Hirotoshi; Nakae, Toshihiro: Engineering problems in machine learning systems (2020)
  4. Wang, Yi; Zhang, Hao; Chae, Kum Ju; Choi, Younhee; Jin, Gong Yong; Ko, Seok-Bum: Novel convolutional neural network architecture for improved pulmonary nodule classification on computed tomography (2020)
  5. Zheng, Qinghe; Tian, Xinyu; Yang, Mingqiang; Wu, Yulin; Su, Huake: PAC-Bayesian framework based drop-path method for 2D discriminative convolutional network pruning (2020)
  6. Cai, Hongmin; Huang, Qinjian; Rong, Wentao; Song, Yan; Li, Jiao; Wang, Jinhua; Chen, Jiazhou; Li, Li: Breast microcalcification diagnosis using deep convolutional neural network from digital mammograms (2019)
  7. Dreossi, Tommaso; Donzé, Alexandre; Seshia, Sanjit A.: Compositional falsification of Cyber-physical systems with machine learning components (2019)
  8. Edgar Riba, Dmytro Mishkin, Daniel Ponsa, Ethan Rublee, Gary Bradski: Kornia: an Open Source Differentiable Computer Vision Library for PyTorch (2019) arXiv
  9. Guo, Yecai; Ye, Fei; Gong, Hao: Learning an efficient convolution neural network for pansharpening (2019)
  10. Higham, Catherine F.; Higham, Desmond J.: Deep learning: an introduction for applied mathematicians (2019)
  11. Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, Zheng Zhang, Dazhi Cheng, Chenchen Zhu, Tianheng Cheng, Qijie Zhao, Buyu Li, Xin Lu, Rui Zhu, Yue Wu, Jifeng Dai, Jingdong Wang, Jianping Shi, Wanli Ouyang, Chen Change Loy, Dahua Lin: MMDetection: Open MMLab Detection Toolbox and Benchmark (2019) arXiv
  12. Kaiyang Zhou, Tao Xiang: Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch (2019) arXiv
  13. Kang, Woochul; Chung, Jaeyong: DeepRT: predictable deep learning inference for cyber-physical systems (2019)
  14. Li, Shan; Deng, Weihong: Reliable crowdsourcing and deep locality-preserving learning for unconstrained facial expression recognition (2019)
  15. Liu, Heng; Fu, Zilin; Han, Jungong; Shao, Ling; Hou, Shudong; Chu, Yuezhong: Single image super-resolution using multi-scale deep encoder-decoder with phase congruency edge map guidance (2019)
  16. Saberian, Mohammad; Vasconcelos, Nuno: Multiclass boosting: margins, codewords, losses, and algorithms (2019)
  17. van den Berg, E.: The Ocean Tensor Package (2019) not zbMATH
  18. Viktor Kazakov, Franz J. Király: Machine Learning Automation Toolbox (MLaut) (2019) arXiv
  19. Wang, Shanshan; Chen, Ying: A joint loss function for deep face recognition (2019)
  20. Xiaomeng Dong, Junpyo Hong, Hsi-Ming Chang, Michael Potter, Aritra Chowdhury, Purujit Bahl, Vivek Soni, Yun-Chan Tsai, Rajesh Tamada, Gaurav Kumar, Caroline Favart, V. Ratna Saripalli, Gopal Avinash: FastEstimator: A Deep Learning Library for Fast Prototyping and Productization (2019) arXiv

1 2 3 next