@article{Sethi_Sankaran_Panwar_Khare_Mani_2018, title={DLPaper2Code: Auto-Generation of Code From Deep Learning Research Papers}, volume={32}, url={https://ojs.aaai.org/index.php/AAAI/article/view/12326}, DOI={10.1609/aaai.v32i1.12326}, abstractNote={ <p> With an abundance of research papers in deep learning, reproducibility or adoption of the existing works becomes a challenge. This is due to the lack of open source implementations provided by the authors. Even if the source code is available, then re-implementing research papers in a different library is a daunting task. To address these challenges, we propose a novel extensible approach, DLPaper2Code, to extract and understand deep learning design flow diagrams and tables available in a research paper and convert them to an abstract computational graph. The extracted computational graph is then converted into execution ready source code in both Keras and Caffe, in real-time. An arXiv-like website is created where the automatically generated designs is made publicly available for 5,000 research papers. The generated designs could be rated and edited using an intuitive drag-and-drop UI framework in a crowd sourced manner. To evaluate our approach, we create a simulated dataset with over 216,000 valid deep learning design flow diagrams using a manually defined grammar. Experiments on the simulated dataset show that the proposed framework provide more than 93% accuracy in flow diagram content extraction. </p> }, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Sethi, Akshay and Sankaran, Anush and Panwar, Naveen and Khare, Shreya and Mani, Senthil}, year={2018}, month={Apr.} }