Fetching the paper…
Reading the bibliography…
Jupyter notebook allows data scientists to write machine learning code together with its documentation in cells.
Codesearchnet challenge: Evaluating the state of semantic code search
Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt. 2019 · 1909
Earlier work this paper cites.
How do data science workers collaborate? roles, workflows, and tools
Amy X Zhang, Michael Muller, and Dakuo Wang. 2020 · 2001
Earlier work this paper cites.
ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Fethi Bougares, Holger Schwenk, and Y. Bengio. 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016 · 2016
Earlier work this paper cites.
Exploring exploratory programming
Mary Beth Kery and Brad A. Myers. 2017 · 2017
Earlier work this paper cites.
Summarizing source code with transferred api knowledge
Xing Hu, Ge Li, Xin Xia, David Lo, Shuai Lu, and Zhi Jin. 2018 · 2018
Earlier work this paper cites.
Exploration and explanation in computational notebooks
Adam Rule, Aurélien Tabard, and James D Hollan. 2018 · 2018
Cited alongside, same era.
Graph2seq: Graph to sequence learning with attention-based neural networks
Kun Xu, Lingfei Wu, Zhiguo Wang, Yansong Feng, Michael Witbrock, and Vadim Sheinin. 2018 · 2018
Cited alongside, same era.
JuICe: A large scale distantly supervised dataset for open domain context-based code generation
Rajas Agashe, Srinivasan Iyer, and Luke Zettlemoyer. 2019 · 2019
Cited alongside, same era.
code2seq: Generating sequences from structured representations of code
Uri Alon, Omer Levy, and Eran Yahav. 2019 · 2019
Cited alongside, same era.
Recommendataions for datasets for source code summarization
A. LeClair and C. McMillan. 2019 · 2019
Cited alongside, same era.
Improved code summarization via a graph neural network
Alexander LeClair, Sakib Haque, Lingfei Wu, and Collin McMillan. 2020 · 2020
Later among the works it cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
Later among the works it cites.
Unsupervised translation of programming languages
Baptiste Roziere, Marie-Anne Lachaux, Lowik Chanussot, and Guillaume Lample. 2020 · 2020
Later among the works it cites.
Autoai: Automating the end-to-end ai lifecycle with humans-in-the-loop
Dakuo Wang, Parikshit Ram, Daniel Karl I Weidele, Sijia Liu, Michael Muller, Justin D Weisz, Abel Valente, Arunima Chaudhary, Dustin Torres, Horst Samulowitz, et al. 2020 · 2020
Later among the works it cites.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Souti Chattopadhyay, Ishita Prasad, Austin Z Henley, Anita Sarma, and Titus Barik. 2020 · 2020
Cited alongside, same era.
Reinforcement learning based graph-to-sequence model for natural question generation
Yu Chen, Lingfei Wu, and Mohammed J Zaki. 2020 · 2020
Cited alongside, same era.
Improved automatic summarization of subroutines via attention to file context
Sakib Haque, Alexander LeClair, Lingfei Wu, and Collin McMillan. 2020 · 2020
Cited alongside, same era.
Themisto: Towards automated documentation generation in computational notebooks
April Yi Wang, Dakuo Wang, Jaimie Drozdal, Michael Muller, Soya Park, Justin D Weisz, Xuye Liu, Lingfei Wu, and Casey Dugan. 2021b
Cited in the paper.
Graph-augmented code summarization in computational notebooks
April Yi Wang, Dakuo Wang, Xuye Liu, Lingfei Wu, et al. 2021c
Cited in the paper.
How much automation does a data scientist want?
Dakuo Wang, Q Vera Liao, Yunfeng Zhang, Udayan Khurana, Horst Samulowitz, Soya Park, Michael Muller, and Lisa Amini. 2021d
Cited in the paper.
Later among the works it cites.
Action word prediction for neural source code summarization
Sakib Haque, Aakash Bansal, Lingfei Wu, and Collin McMillan. 2021 · 2021
Closest in time.
What makes a well-documented notebook? a case study of data scientists’ documentation practices in kaggle
April Yi Wang, Dakuo Wang, Jaimie Drozdal, Xuye Liu, Soya Park, Steve Oney, and Christopher Brooks. 2021a · 2021
Closest in time.
Summarizing source code using a neural attention model
Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, and Luke Zettlemoyer. 2016 · 2083
Closest in time.