Dragon: A Computation Graph Virtual Machine Based Deep Learning Framework

2017/07/26 by Ting Pan, Pan, Ting
Computer Science · #Advanced Graph Neural Networks #Advanced Neural Network Applications #FOS: Computer and information sciences #Graph Theory and Algorithms #Machine Learning (cs.LG) #Mathematical Software (cs.MS) #Neural and Evolutionary Computing (cs.NE) #Software Engineering (cs.SE)

paper · pdf · doi:10.48550/arxiv.1707.08265

openalex publication_date 2017/07/26 · openalex created_date 2017/07/31 · openalex updated_date 2026/07/28

Abstract

Deep Learning has made a great progress for these years. However, it is still difficult to master the implement of various models because different researchers may release their code based on different frameworks or interfaces. In this paper, we proposed a computation graph based framework which only aims to introduce well-known interfaces. It will help a lot when reproducing a newly model or transplanting models that were implemented by other frameworks. Additionally, we implement numerous recent models covering both Computer Vision and Nature Language Processing. We demonstrate that our framework will not suffer from model-starving because it is much easier to make full use of the works that are already done.

Citations

• Adam: A Method for Stochastic Optimization
• Long Short-Term Memory
• Finding Structure in Time
• MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems

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