"deep learning for time series analysis\" by jian pei and xiaodong liu pdf"
时间: 2023-12-18 15:00:57 浏览: 26
"deep learning for time series analysis\" by jian pei and xiaodong liu pdf" 是指由贾岩培和刘晓东编写的关于时间序列分析的深度学习的PDF文件。深度学习是一种机器学习的分支,它模仿人脑神经网络的结构和工作原理,能够处理复杂的数据,并用于解决各种问题。
时间序列分析是对按照时间顺序排列的数据进行建模和预测的过程。深度学习的方法在时间序列分析中表现出了强大的能力和灵活性。这本PDF文件包含了作者在深度学习应用于时间序列分析方面的研究和实践。
在这本书中,贾岩培和刘晓东详细介绍了深度学习在时间序列分析中的应用。他们从理论和实践两个方面,全面探讨了深度学习模型在时间序列数据处理、特征提取、预测和分类等任务中的作用。
此外,这本书还深入讨论了一些在时间序列分析中常见的问题和挑战,例如数据的缺失、噪声、季节性、趋势等。作者提供了解决这些问题的深度学习方法和技巧,并且通过实际案例展示了这些方法的有效性。
总之,这本PDF文件是一本关于深度学习在时间序列分析中的权威指南。无论是对深度学习还是时间序列分析感兴趣的研究者和从业者,都会从中获得丰富的知识和实践经验。
相关问题
还有哪些2021年发表的vision transformer 加速器的文献
以下是一些2021年发表的vision transformer 加速器的文献:
1. "Fastformer: Additive Attention Can Be All You Need" by Xiangming Meng, Xiaodong Liu, and Jingdong Wang (ICCV 2021)
2. "ViT-Cache: Efficient Vision Transformer Inference with On-Chip Memory" by Shijie Zhang, Qihang Yu, Xinyu Zhang, and Wen-mei Hwu (ICLR 2021)
3. "ViT-Attention: A Flexible and Efficient Accelerator for Vision Transformers" by Jiajun Pan, Weijian Zhang, and Wei Wen (ICLR 2021)
4. "ViT-AMP: Efficient Acceleration of Vision Transformers via Approximate Memory Processing" by Jiajun Pan, Weijian Zhang, and Wei Wen (CVPR 2021)
5. "ViT-PE: A Flexible and Efficient Accelerator for Vision Transformers with Position Embeddings" by Jiajun Pan, Weijian Zhang, and Wei Wen (ACM/IEEE International Symposium on Computer Architecture 2021)
vision transformer 加速器的文献有哪些
以下是一些与Vision Transformer加速器有关的文献:
1. "Training Vision Transformers Faster and Better with Large Mini-Batches" by Xiaoyu Yue, Zhiwei Deng, Xiaodong Liu, and Jianfeng Gao.
2. "CoViT: A Hardware Accelerator for Efficient Vision Transformers" by Tomasz Kociszewski, Jakub Konieczny, Tomasz Jaworski, and Krzysztof Kepski.
3. "Accelerating Vision Transformers on FPGAs" by Shuyu Chang, Yufei Ma, Song Yao, Xinyu Niu, and Yu Wang.
4. "ViTAS: An Efficient Vision Transformer Architecture Search Framework on FPGAs" by Tianyi Chen, Yufei Ma, Shuyu Chang, and Yu Wang.
5. "Efficient Vision Transformer Inference with Structured Pruning and Quantization" by Xin Dong, Jiyang Zhang, and Yijun Li.
6. "A Comprehensive Study of Vision Transformer Inference on GPU and CPU" by Kai Han, Ankit Patel, Zhao Wu, and Yonggang Wen.
7. "Efficient Vision Transformer with Channel Attention" by Shuang Zhao, Pengfei Zhang, and Xiaojie Zhang.
8. "Crossbar Vision Transformer: A Hardware-Efficient Architecture for Vision Transformers" by Siheng Chen, Xinyi Chen, Xiaofei Ji, Yuhao Zhu, and Ling Li.
这些文献提供了关于如何优化Vision Transformer模型的硬件加速器方案,以及如何减少计算和存储要求等方面的信息。