Learning to Complete Code with Sketches
Daya Guo, Alexey Svyatkovskiy, Jian Yin, Nan Duan, Marc Brockschmidt, Miltos Allamanis
ICLR 2022 | April 2022
Deep Program Understanding
Teaching machines to understand complex algorithms
论文与出版物
按年份分类的论文与出版物
-
HEAT: Hyperedge Attention Networks
Dobrik Georgiev, Marc Brockschmidt, Miltos Allamanis
January 2022
-
Self-Supervised Bug Detection and Repair
Miltos Allamanis, Henry Jackson-Flux, Marc Brockschmidt
NeurIPS 2021 | May 2021
Copy that! Editing Sequences by Copying Spans
Sheena Panthaplackel, Miltos Allamanis, Marc Brockschmidt
AAAI | January 2021
-
Typilus: Neural Type Hints
Miltos Allamanis, Earl T. Barr, Soline Ducousso, Zheng Gao
2020 Programming Language Design and Implementation | June 2020
Fast and Memory-Efficient Neural Code Completion
Alexey Svyatkovskiy, Sebastian Lee, Anna Hadjitofi, Maik Riechert, Juliana Vicente Franco, Miltos Allamanis
Mining Software Repositories | April 2020
-
DIRE: a neural approach to decompiled identifier naming
Jeremy Lacomis, Pengcheng Yin, Edward J. Schwartz, Miltos Allamanis, Claire Le Goues, Graham Neubig, Bogdan Vasilescu
2019 Automated Software Engineering | November 2019
The adverse effects of code duplication in machine learning models of code
Miltos Allamanis
2019 SIGPLAN symposium on New ideas, new paradigms, and reflections on programming and software | October 2019
DOI Publication Publication Publication Publication Publication
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search.
Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltos Allamanis, Marc Brockschmidt
MSR-TR-2019-45 | September 2019
作者：Microsoft
Generative Code Modeling with Graphs
Marc Brockschmidt, Miltos Allamanis, Alex Gaunt, Alex Polozov
7th International Conference on Learning Representations | May 2019
Learning to Represent Edits
Pengcheng Yin, Graham Neubig, Miltiadis Allamanis, Marc Brockschmidt, Alexander Gaunt
ICLR 2019 | May 2019
arXiv:1810.13337 [cs.LG]
-
Constrained Graph Variational Autoencoders for Molecule Design
Qi Liu, Miltos Allamanis, Marc Brockschmidt, Alexander Gaunt
NIPS 2018 | December 2018
Deep learning type inference
Vincent J. Hellendoorn, Christian Bird, Earl T. Barr, Miltos Allamanis
2018 Foundations of Software Engineering | October 2018
RefiNym: using names to refine types
Santanu Kumar Dash, Miltos Allamanis, Earl T. Barr
2018 Foundations of Software Engineering | October 2018
Structured Neural Summarization
Patrick Fernandes, Miltos Allamanis, Marc Brockschmidt
2018 International Conference on Learning Representations | September 2018
Robust Text-to-SQL Generation with Execution-Guided Decoding
Chenglong Wang, Kedar Tatwawadi, Marc Brockschmidt, Po-Sen Huang, Yi Mao, Oleksandr Polozov, Rishabh Singh
September 2018
A Survey of Machine Learning for Big Code and Naturalness
Miltos Allamanis, Earl T. Barr, Premkumar Devanbu, Charles Sutton
ACM Computing Surveys | July 2018, 第 51 卷(4): pp. 81
-
Semantics-aware Program Sampling
Pratiksha Thaker, Daniel Tarlow, Marc Brockschmidt, Alexander Gaunt
MSR-TR-2017-47 | November 2017
Graph Partition Neural Networks for Semi-Supervised Classification
Renjie Liao, Marc Brockschmidt (mabrocks), Daniel Tarlow, Alexander Gaunt, Raquel Urtasun, Richard S. Zemel
MSR-TR-2017-46 | November 2017
作者：Microsoft
Learning to Represent Programs with Graphs
Miltos Allamanis, Marc Brockschmidt, Mahmoud Khademi
International Conference on Learning Representations 2018 | November 2017
Pointing Out SQL Queries From Text
Chenglong Wang, Marc Brockschmidt, Rishabh Singh
MSR-TR-2017-45 | November 2017
Differentiable Programs with Neural Libraries
Alexander Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow
Proceedings of ICML’17 | July 2017
Learning Shape Analysis
Marc Brockschmidt, Yuxin Chen, Pushmeet Kohli, Siddharth Krishna, Daniel Tarlow
Proceedings of SAS’17 | July 2017
SmartPaste: Learning to Adapt Source Code
Miltos Allamanis, Marc Brockschmidt
May 2017
Neural Program Lattices
Chengtao Li, Daniel Tarlow, Alexander Gaunt, Marc Brockschmidt, Nate Kushman
Proceedings of ICLR’17 | March 2017
DeepCoder: Learning to Write Programs
Matej Balog, Alexander Gaunt, Marc Brockschmidt, Sebastian Nowozin, Daniel Tarlow
Proceedings of ICLR’17 | March 2017
Differentiable Functional Program Interpreters
John K. Feser, Marc Brockschmidt, Alexander Gaunt, Daniel Tarlow
March 2017
-
Mining Semantic Loop Idioms from Big Code
Miltiadis Allamanis, Earl T. Barr, Christian Bird, Premkumar Devanbu, Mark Marron, Charles Sutton
MSR-TR-2016-1116 | November 2016
TerpreT: A Probabilistic Programming Language for Program Induction
Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Daniel Tarlow
August 2016
Best paper at NIPS Workshop on Neural Abstract Machines and Program Induction (NAMPI 2016)
Gated Graph Sequence Neural Networks
Yujia Li, Richard Zemel, Marc Brockschmidt, Daniel Tarlow
Proceedings of ICLR’16 | April 2016
SWIM: Synthesizing What I Mean
Mukund Raghothaman, Yi Wei, Youssef Hamadi
ICSE 2016 | February 2016
A Convolutional Attention Network for Extreme Summarization of Source Code
Miltos Allamanis, Hao Peng, Charles Sutton
2016 International Conference on Machine Learning | February 2016
-
Suggesting accurate method and class names
Miltos Allamanis, Earl T. Barr, Christian Bird, Charles Sutton
2015 Foundations of Software Engineering | August 2015
Bimodal Modelling of Source Code and Natural Language
Miltos Allamanis, Daniel Tarlow, Andy Gordon, Yi Wei
International Conference on Machine Learning | August 2015
Learning to Decipher the Heap for Program Verification
Marc Brockschmidt, Yuxin Chen, Byron Cook, Pushmeet Kohli, Daniel Tarlow
ICML 2015 Proceedings of the Constructive Machine Learning | July 2015
-
Learning Natural Coding Conventions
Miltiadis Allamanis, Earl T. Barr, Christian Bird, Charles Sutton
Proceedings of the ACM SigSoft Symposium on Foundations of Software Engineering | November 2014
Structured Generative Models of Natural Source Code
Chris J. Maddison, Daniel Tarlow
The 31st International Conference on Machine Learning (ICML) | June 2014
按研究领域分类的论文与出版物
-
Learning to Complete Code with Sketches
Daya Guo, Alexey Svyatkovskiy, Jian Yin, Nan Duan, Marc Brockschmidt, Miltos Allamanis
ICLR 2022 | April 2022
HEAT: Hyperedge Attention Networks
Dobrik Georgiev, Marc Brockschmidt, Miltos Allamanis
January 2022
Self-Supervised Bug Detection and Repair
Miltos Allamanis, Henry Jackson-Flux, Marc Brockschmidt
NeurIPS 2021 | May 2021
Copy that! Editing Sequences by Copying Spans
Sheena Panthaplackel, Miltos Allamanis, Marc Brockschmidt
AAAI | January 2021
Typilus: Neural Type Hints
Miltos Allamanis, Earl T. Barr, Soline Ducousso, Zheng Gao
2020 Programming Language Design and Implementation | June 2020
Fast and Memory-Efficient Neural Code Completion
Alexey Svyatkovskiy, Sebastian Lee, Anna Hadjitofi, Maik Riechert, Juliana Vicente Franco, Miltos Allamanis
Mining Software Repositories | April 2020
The adverse effects of code duplication in machine learning models of code
Miltos Allamanis
2019 SIGPLAN symposium on New ideas, new paradigms, and reflections on programming and software | October 2019
DOI Publication Publication Publication Publication Publication
Generative Code Modeling with Graphs
Marc Brockschmidt, Miltos Allamanis, Alex Gaunt, Alex Polozov
7th International Conference on Learning Representations | May 2019
Learning to Represent Edits
Pengcheng Yin, Graham Neubig, Miltiadis Allamanis, Marc Brockschmidt, Alexander Gaunt
ICLR 2019 | May 2019
arXiv:1810.13337 [cs.LG]
Constrained Graph Variational Autoencoders for Molecule Design
Qi Liu, Miltos Allamanis, Marc Brockschmidt, Alexander Gaunt
NIPS 2018 | December 2018
Structured Neural Summarization
Patrick Fernandes, Miltos Allamanis, Marc Brockschmidt
2018 International Conference on Learning Representations | September 2018
Robust Text-to-SQL Generation with Execution-Guided Decoding
Chenglong Wang, Kedar Tatwawadi, Marc Brockschmidt, Po-Sen Huang, Yi Mao, Oleksandr Polozov, Rishabh Singh
September 2018
A Survey of Machine Learning for Big Code and Naturalness
Miltos Allamanis, Earl T. Barr, Premkumar Devanbu, Charles Sutton
ACM Computing Surveys | July 2018, 第 51 卷(4): pp. 81
Semantics-aware Program Sampling
Pratiksha Thaker, Daniel Tarlow, Marc Brockschmidt, Alexander Gaunt
MSR-TR-2017-47 | November 2017
Graph Partition Neural Networks for Semi-Supervised Classification
Renjie Liao, Marc Brockschmidt (mabrocks), Daniel Tarlow, Alexander Gaunt, Raquel Urtasun, Richard S. Zemel
MSR-TR-2017-46 | November 2017
作者：Microsoft
Pointing Out SQL Queries From Text
Chenglong Wang, Marc Brockschmidt, Rishabh Singh
MSR-TR-2017-45 | November 2017
Learning to Represent Programs with Graphs
Miltos Allamanis, Marc Brockschmidt, Mahmoud Khademi
International Conference on Learning Representations 2018 | November 2017
Differentiable Programs with Neural Libraries
Alexander Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow
Proceedings of ICML’17 | July 2017
Learning Shape Analysis
Marc Brockschmidt, Yuxin Chen, Pushmeet Kohli, Siddharth Krishna, Daniel Tarlow
Proceedings of SAS’17 | July 2017
SmartPaste: Learning to Adapt Source Code
Miltos Allamanis, Marc Brockschmidt
May 2017
Neural Program Lattices
Chengtao Li, Daniel Tarlow, Alexander Gaunt, Marc Brockschmidt, Nate Kushman
Proceedings of ICLR’17 | March 2017
Differentiable Functional Program Interpreters
John K. Feser, Marc Brockschmidt, Alexander Gaunt, Daniel Tarlow
March 2017
DeepCoder: Learning to Write Programs
Matej Balog, Alexander Gaunt, Marc Brockschmidt, Sebastian Nowozin, Daniel Tarlow
Proceedings of ICLR’17 | March 2017
Mining Semantic Loop Idioms from Big Code
Miltiadis Allamanis, Earl T. Barr, Christian Bird, Premkumar Devanbu, Mark Marron, Charles Sutton
MSR-TR-2016-1116 | November 2016
TerpreT: A Probabilistic Programming Language for Program Induction
Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Daniel Tarlow
August 2016
Best paper at NIPS Workshop on Neural Abstract Machines and Program Induction (NAMPI 2016)
Gated Graph Sequence Neural Networks
Yujia Li, Richard Zemel, Marc Brockschmidt, Daniel Tarlow
Proceedings of ICLR’16 | April 2016
SWIM: Synthesizing What I Mean
Mukund Raghothaman, Yi Wei, Youssef Hamadi
ICSE 2016 | February 2016
A Convolutional Attention Network for Extreme Summarization of Source Code
Miltos Allamanis, Hao Peng, Charles Sutton
2016 International Conference on Machine Learning | February 2016
Bimodal Modelling of Source Code and Natural Language
Miltos Allamanis, Daniel Tarlow, Andy Gordon, Yi Wei
International Conference on Machine Learning | August 2015
Structured Generative Models of Natural Source Code
Chris J. Maddison, Daniel Tarlow
The 31st International Conference on Machine Learning (ICML) | June 2014
-
Learning to Complete Code with Sketches
Daya Guo, Alexey Svyatkovskiy, Jian Yin, Nan Duan, Marc Brockschmidt, Miltos Allamanis
ICLR 2022 | April 2022
HEAT: Hyperedge Attention Networks
Dobrik Georgiev, Marc Brockschmidt, Miltos Allamanis
January 2022
Self-Supervised Bug Detection and Repair
Miltos Allamanis, Henry Jackson-Flux, Marc Brockschmidt
NeurIPS 2021 | May 2021
Typilus: Neural Type Hints
Miltos Allamanis, Earl T. Barr, Soline Ducousso, Zheng Gao
2020 Programming Language Design and Implementation | June 2020
Fast and Memory-Efficient Neural Code Completion
Alexey Svyatkovskiy, Sebastian Lee, Anna Hadjitofi, Maik Riechert, Juliana Vicente Franco, Miltos Allamanis
Mining Software Repositories | April 2020
DIRE: a neural approach to decompiled identifier naming
Jeremy Lacomis, Pengcheng Yin, Edward J. Schwartz, Miltos Allamanis, Claire Le Goues, Graham Neubig, Bogdan Vasilescu
2019 Automated Software Engineering | November 2019
The adverse effects of code duplication in machine learning models of code
Miltos Allamanis
2019 SIGPLAN symposium on New ideas, new paradigms, and reflections on programming and software | October 2019
DOI Publication Publication Publication Publication Publication
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search.
Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltos Allamanis, Marc Brockschmidt
MSR-TR-2019-45 | September 2019
作者：Microsoft
Generative Code Modeling with Graphs
Marc Brockschmidt, Miltos Allamanis, Alex Gaunt, Alex Polozov
7th International Conference on Learning Representations | May 2019
Learning to Represent Edits
Pengcheng Yin, Graham Neubig, Miltiadis Allamanis, Marc Brockschmidt, Alexander Gaunt
ICLR 2019 | May 2019
arXiv:1810.13337 [cs.LG]
Constrained Graph Variational Autoencoders for Molecule Design
Qi Liu, Miltos Allamanis, Marc Brockschmidt, Alexander Gaunt
NIPS 2018 | December 2018
Deep learning type inference
Vincent J. Hellendoorn, Christian Bird, Earl T. Barr, Miltos Allamanis
2018 Foundations of Software Engineering | October 2018
RefiNym: using names to refine types
Santanu Kumar Dash, Miltos Allamanis, Earl T. Barr
2018 Foundations of Software Engineering | October 2018
Semantics-aware Program Sampling
Pratiksha Thaker, Daniel Tarlow, Marc Brockschmidt, Alexander Gaunt
MSR-TR-2017-47 | November 2017
Pointing Out SQL Queries From Text
Chenglong Wang, Marc Brockschmidt, Rishabh Singh
MSR-TR-2017-45 | November 2017
Learning to Represent Programs with Graphs
Miltos Allamanis, Marc Brockschmidt, Mahmoud Khademi
International Conference on Learning Representations 2018 | November 2017
Differentiable Programs with Neural Libraries
Alexander Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow
Proceedings of ICML’17 | July 2017
Learning Shape Analysis
Marc Brockschmidt, Yuxin Chen, Pushmeet Kohli, Siddharth Krishna, Daniel Tarlow
Proceedings of SAS’17 | July 2017
SmartPaste: Learning to Adapt Source Code
Miltos Allamanis, Marc Brockschmidt
May 2017
Neural Program Lattices
Chengtao Li, Daniel Tarlow, Alexander Gaunt, Marc Brockschmidt, Nate Kushman
Proceedings of ICLR’17 | March 2017
Differentiable Functional Program Interpreters
John K. Feser, Marc Brockschmidt, Alexander Gaunt, Daniel Tarlow
March 2017
DeepCoder: Learning to Write Programs
Matej Balog, Alexander Gaunt, Marc Brockschmidt, Sebastian Nowozin, Daniel Tarlow
Proceedings of ICLR’17 | March 2017
Mining Semantic Loop Idioms from Big Code
Miltiadis Allamanis, Earl T. Barr, Christian Bird, Premkumar Devanbu, Mark Marron, Charles Sutton
MSR-TR-2016-1116 | November 2016
TerpreT: A Probabilistic Programming Language for Program Induction
Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Daniel Tarlow
August 2016
Best paper at NIPS Workshop on Neural Abstract Machines and Program Induction (NAMPI 2016)
SWIM: Synthesizing What I Mean
Mukund Raghothaman, Yi Wei, Youssef Hamadi
ICSE 2016 | February 2016
Suggesting accurate method and class names
Miltos Allamanis, Earl T. Barr, Christian Bird, Charles Sutton
2015 Foundations of Software Engineering | August 2015
Bimodal Modelling of Source Code and Natural Language
Miltos Allamanis, Daniel Tarlow, Andy Gordon, Yi Wei
International Conference on Machine Learning | August 2015
Learning to Decipher the Heap for Program Verification
Marc Brockschmidt, Yuxin Chen, Byron Cook, Pushmeet Kohli, Daniel Tarlow
ICML 2015 Proceedings of the Constructive Machine Learning | July 2015
Learning Natural Coding Conventions
Miltiadis Allamanis, Earl T. Barr, Christian Bird, Charles Sutton
Proceedings of the ACM SigSoft Symposium on Foundations of Software Engineering | November 2014
按类型分类的论文与出版物
-
Learning to Complete Code with Sketches
Daya Guo, Alexey Svyatkovskiy, Jian Yin, Nan Duan, Marc Brockschmidt, Miltos Allamanis
ICLR 2022 | April 2022
Self-Supervised Bug Detection and Repair
Miltos Allamanis, Henry Jackson-Flux, Marc Brockschmidt
NeurIPS 2021 | May 2021
Copy that! Editing Sequences by Copying Spans
Sheena Panthaplackel, Miltos Allamanis, Marc Brockschmidt
AAAI | January 2021
Typilus: Neural Type Hints
Miltos Allamanis, Earl T. Barr, Soline Ducousso, Zheng Gao
2020 Programming Language Design and Implementation | June 2020
Fast and Memory-Efficient Neural Code Completion
Alexey Svyatkovskiy, Sebastian Lee, Anna Hadjitofi, Maik Riechert, Juliana Vicente Franco, Miltos Allamanis
Mining Software Repositories | April 2020
DIRE: a neural approach to decompiled identifier naming
Jeremy Lacomis, Pengcheng Yin, Edward J. Schwartz, Miltos Allamanis, Claire Le Goues, Graham Neubig, Bogdan Vasilescu
2019 Automated Software Engineering | November 2019
The adverse effects of code duplication in machine learning models of code
Miltos Allamanis
2019 SIGPLAN symposium on New ideas, new paradigms, and reflections on programming and software | October 2019
DOI Publication Publication Publication Publication Publication
Generative Code Modeling with Graphs
Marc Brockschmidt, Miltos Allamanis, Alex Gaunt, Alex Polozov
7th International Conference on Learning Representations | May 2019
Learning to Represent Edits
Pengcheng Yin, Graham Neubig, Miltiadis Allamanis, Marc Brockschmidt, Alexander Gaunt
ICLR 2019 | May 2019
arXiv:1810.13337 [cs.LG]
Constrained Graph Variational Autoencoders for Molecule Design
Qi Liu, Miltos Allamanis, Marc Brockschmidt, Alexander Gaunt
NIPS 2018 | December 2018
Deep learning type inference
Vincent J. Hellendoorn, Christian Bird, Earl T. Barr, Miltos Allamanis
2018 Foundations of Software Engineering | October 2018
RefiNym: using names to refine types
Santanu Kumar Dash, Miltos Allamanis, Earl T. Barr
2018 Foundations of Software Engineering | October 2018
Structured Neural Summarization
Patrick Fernandes, Miltos Allamanis, Marc Brockschmidt
2018 International Conference on Learning Representations | September 2018
Learning to Represent Programs with Graphs
Miltos Allamanis, Marc Brockschmidt, Mahmoud Khademi
International Conference on Learning Representations 2018 | November 2017
Differentiable Programs with Neural Libraries
Alexander Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow
Proceedings of ICML’17 | July 2017
Learning Shape Analysis
Marc Brockschmidt, Yuxin Chen, Pushmeet Kohli, Siddharth Krishna, Daniel Tarlow
Proceedings of SAS’17 | July 2017
Neural Program Lattices
Chengtao Li, Daniel Tarlow, Alexander Gaunt, Marc Brockschmidt, Nate Kushman
Proceedings of ICLR’17 | March 2017
Differentiable Functional Program Interpreters
John K. Feser, Marc Brockschmidt, Alexander Gaunt, Daniel Tarlow
March 2017
DeepCoder: Learning to Write Programs
Matej Balog, Alexander Gaunt, Marc Brockschmidt, Sebastian Nowozin, Daniel Tarlow
Proceedings of ICLR’17 | March 2017
Gated Graph Sequence Neural Networks
Yujia Li, Richard Zemel, Marc Brockschmidt, Daniel Tarlow
Proceedings of ICLR’16 | April 2016
SWIM: Synthesizing What I Mean
Mukund Raghothaman, Yi Wei, Youssef Hamadi
ICSE 2016 | February 2016
A Convolutional Attention Network for Extreme Summarization of Source Code
Miltos Allamanis, Hao Peng, Charles Sutton
2016 International Conference on Machine Learning | February 2016
Suggesting accurate method and class names
Miltos Allamanis, Earl T. Barr, Christian Bird, Charles Sutton
2015 Foundations of Software Engineering | August 2015
Bimodal Modelling of Source Code and Natural Language
Miltos Allamanis, Daniel Tarlow, Andy Gordon, Yi Wei
International Conference on Machine Learning | August 2015
Learning to Decipher the Heap for Program Verification
Marc Brockschmidt, Yuxin Chen, Byron Cook, Pushmeet Kohli, Daniel Tarlow
ICML 2015 Proceedings of the Constructive Machine Learning | July 2015
Learning Natural Coding Conventions
Miltiadis Allamanis, Earl T. Barr, Christian Bird, Charles Sutton
Proceedings of the ACM SigSoft Symposium on Foundations of Software Engineering | November 2014
Structured Generative Models of Natural Source Code
Chris J. Maddison, Daniel Tarlow
The 31st International Conference on Machine Learning (ICML) | June 2014
-
HEAT: Hyperedge Attention Networks
Dobrik Georgiev, Marc Brockschmidt, Miltos Allamanis
January 2022
-
Robust Text-to-SQL Generation with Execution-Guided Decoding
Chenglong Wang, Kedar Tatwawadi, Marc Brockschmidt, Po-Sen Huang, Yi Mao, Oleksandr Polozov, Rishabh Singh
September 2018
SmartPaste: Learning to Adapt Source Code
Miltos Allamanis, Marc Brockschmidt
May 2017
TerpreT: A Probabilistic Programming Language for Program Induction
Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Daniel Tarlow
August 2016
Best paper at NIPS Workshop on Neural Abstract Machines and Program Induction (NAMPI 2016)
-
A Survey of Machine Learning for Big Code and Naturalness
Miltos Allamanis, Earl T. Barr, Premkumar Devanbu, Charles Sutton
ACM Computing Surveys | July 2018, 第 51 卷(4): pp. 81
-
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search.
Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltos Allamanis, Marc Brockschmidt
MSR-TR-2019-45 | September 2019
作者：Microsoft
Semantics-aware Program Sampling
Pratiksha Thaker, Daniel Tarlow, Marc Brockschmidt, Alexander Gaunt
MSR-TR-2017-47 | November 2017
Graph Partition Neural Networks for Semi-Supervised Classification
Renjie Liao, Marc Brockschmidt (mabrocks), Daniel Tarlow, Alexander Gaunt, Raquel Urtasun, Richard S. Zemel
MSR-TR-2017-46 | November 2017
作者：Microsoft
Pointing Out SQL Queries From Text
Chenglong Wang, Marc Brockschmidt, Rishabh Singh
MSR-TR-2017-45 | November 2017
Mining Semantic Loop Idioms from Big Code
Miltiadis Allamanis, Earl T. Barr, Christian Bird, Premkumar Devanbu, Mark Marron, Charles Sutton
MSR-TR-2016-1116 | November 2016