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 78 articles )

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  1. Liu, Bo; Shen, Mengya: Some geometrical and topological properties of DNNs’ decision boundaries (2022)
  2. Bertsimas, Dimitris; Stellato, Bartolomeo: The voice of optimization (2021)
  3. Duchi, John C.; Namkoong, Hongseok: Learning models with uniform performance via distributionally robust optimization (2021)
  4. Hyde, David A. B.; Bao, Michael; Fedkiw, Ronald: On obtaining sparse semantic solutions for inverse problems, control, and neural network training (2021)
  5. Lakhmiri, Dounia; Digabel, Sébastien Le; Tribes, Christophe: HyperNOMAD. Hyperparameter optimization of deep neural networks using mesh adaptive direct search (2021)
  6. Meng, Chuangji; Xu, Cunlu; Lei, Qin; Su, Wei; Wu, Jinzhao: Balanced joint maximum mean discrepancy for deep transfer learning (2021)
  7. Sakai, Tomoya; Niu, Gang; Sugiyama, Masashi: Information-theoretic representation learning for positive-unlabeled classification (2021)
  8. Vincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu, Antonio Carta, Gabriele Graffieti, Tyler L. Hayes, Matthias De Lange, Marc Masana, Jary Pomponi, Gido van de Ven, Martin Mundt, Qi She, Keiland Cooper, Jeremy Forest, Eden Belouadah, Simone Calderara, German I. Parisi, Fabio Cuzzolin, Andreas Tolias, Simone Scardapane, Luca Antiga, Subutai Amhad, Adrian Popescu, Christopher Kanan, Joost van de Weijer, Tinne Tuytelaars, Davide Bacciu, Davide Maltoni: Avalanche: an End-to-End Library for Continual Learning (2021) arXiv
  9. Zhu, Qiming; Liu, Zeliang; Yan, Jinhui: Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks (2021)
  10. 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)
  11. Chen, Zixuan; Xu, Zekai; Gui, Qiuling; Yang, Xin; Cheng, Qimin; Hou, Wenguang; Ding, Mingyue: Self-learning based medical image representation for rigid real-time and multimodal slice-to-volume registration (2020)
  12. 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)
  13. Kovač, Ivan; Marák, Pavol: Openfinger: towards a combination of discriminative power of fingerprints and finger vein patterns in multimodal biometric system (2020)
  14. Kuwajima, Hiroshi; Yasuoka, Hirotoshi; Nakae, Toshihiro: Engineering problems in machine learning systems (2020)
  15. Li, Aoxue; Lu, Zhiwu; Guan, Jiechao; Xiang, Tao; Wang, Liwei; Wen, Ji-Rong: Transferrable feature and projection learning with class hierarchy for zero-shot learning (2020)
  16. Liu, Haomiao; Wang, Ruiping; Shan, Shiguang; Chen, Xilin: Learning multifunctional binary codes for personalized image retrieval (2020)
  17. Liu, Li; Ouyang, Wanli; Wang, Xiaogang; Fieguth, Paul; Chen, Jie; Liu, Xinwang; Pietikäinen, Matti: Deep learning for generic object detection: a survey (2020)
  18. Sharma, Vipul; Mir, Roohie Naaz: A comprehensive and systematic look up into deep learning based object detection techniques: a review (2020)
  19. Tian, Chunwei; Fei, Lunke; Zheng, Wenxian; Xu, Yong; Zuo, Wangmeng; Lin, Chia-Wen: Deep learning on image denoising: an overview (2020)
  20. Wang, Xiang; Liu, Sifei; Ma, Huimin; Yang, Ming-Hsuan: Weakly-supervised semantic segmentation by iterative affinity learning (2020)

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