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Code based Language Models (LMs) have shown very promising results in the field of software engineering with applications such as code refinement, code completion and generation.
Codesearchnet challenge: Evaluating the state of semantic code search
Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt · 1909
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Learning the parts of objects by non-negative matrix factorization
Daniel D. Lee and H. Sebastian Seung · 1999
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Longformer: The long-document transformer
Iz Beltagy, Matthew E Peters, and Arman Cohan · 2004
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Graphcodebert: Pre-training code representations with data flow
Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, et al · 2009
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Nmf-based multimodal image indexing for querying by visual example
Fabio A. González, Juan C. Caicedo, Olfa Nasraoui, and Jaafar Ben-Abdallah · 2010
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big-O python module
Pietro Berkes, Finico, and Svisser etc · 2011
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Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Training deep nets with sublinear memory cost
Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin · 2016
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Complexity analysis for java with aprove
Florian Frohn and Jürgen Giesl · 2017
Cited alongside, same era.
Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, et al · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Cited alongside, same era.
Guesscompx: An empirical complexity estimation in r
Marc Agenis-Nevers, Neeraj Dhanraj Bokde, Zaher Mundher Yaseen, and Mayur Shende · 2019
Cited alongside, same era.
Learning based methods for code runtime complexity prediction
Jagriti Sikka, Kushal Satya, Yaman Kumar, Shagun Uppal, Rajiv Ratn Shah, and Roger Zimmermann · 2020
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Big-O-Calculator
Seok Won · 2020
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Ecco: An open source library for the explainability of transformer language models
J Alammar · 2021
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Retrieval augmented code generation and summarization
Md Rizwan Parvez, Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang · 2021
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Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation
Yue Wang, Weishi Wang, Shafiq Joty, and Steven C.H. Hoi · 2021
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End-to-end multiple instance learning with gradient accumulation
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Cited alongside, same era.
CodeBERT: A pre-trained model for programming and natural languages
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, and Ming Zhou · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Big o notation calculator, 2020
Alec Shunnarah · 2020
Cited alongside, same era.
Axel Andersson, Nadezhda Koriakina, Nataša Sladoje, and Joakim Lindblad · 2022
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Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine
Ryuji Hamamoto, Ken Takasawa, Hidenori Machino, Kazuma Kobayashi, Satoshi Takahashi, Amina Bolatkan, Norio Shinkai, Akira Sakai, Rina Aoyama, Masayoshi Yamada, Ken Asada, Masaaki Komatsu, Koji Okamoto, Hirokazu Kameoka, and Syuzo Kaneko · 2022
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Deep learning-based code complexity prediction
Mingi Jeon, Seung-Yeop Baik, Joonghyuk Hahn, Yo-Sub Han, and Sang-Ki Ko · 2022
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Text and code embeddings by contrastive pre-training
Arvind Neelakantan, Tao Xu, Raul Puri, Alec Radford, Jesse Michael Han, Jerry Tworek, Qiming Yuan, Nikolas Tezak, Jong Wook Kim, Chris Hallacy, et al · 2022
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