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Conference and Workshop Papers
- 2023
[c6]Alexander Brauckmann, Elizabeth Polgreen, Tobias Grosser
, Michael F. P. O'Boyle:
mlirSynth: Automatic, Retargetable Program Raising in Multi-Level IR Using Program Synthesis. PACT 2023: 39-50- 2022
- [c5]Jordi Armengol-Estapé, Jackson Woodruff, Alexander Brauckmann, José Wesley de Souza Magalhães, Michael F. P. O'Boyle:
ExeBench: an ML-scale dataset of executable C functions. MAPS@PLDI 2022: 50-59 - 2021
- [c4]Alexander Brauckmann, Andrés Goens, Jerónimo Castrillón:
PolyGym: Polyhedral Optimizations as an Environment for Reinforcement Learning. PACT 2021: 17-29 - 2020
- [c3]Alexander Brauckmann, Andrés Goens, Sebastian Ertel, Jerónimo Castrillón:
Compiler-based graph representations for deep learning models of code. CC 2020: 201-211 - [c2]Alexander Brauckmann, Andrés Goens, Jerónimo Castrillón:
ComPy-Learn: A toolbox for exploring machine learning representations for compilers. FDL 2020: 1-4 - 2019
- [c1]Andrés Goens, Alexander Brauckmann, Sebastian Ertel, Chris Cummins, Hugh Leather, Jerónimo Castrillón:
A case study on machine learning for synthesizing benchmarks. MAPL@PLDI 2019: 38-46
Informal and Other Publications
- 2023
- [i3]Jackson Woodruff, Thomas Koehler, Alexander Brauckmann, Chris Cummins, Sam Ainsworth, Michael F. P. O'Boyle:
Rewriting History: Repurposing Domain-Specific CGRAs. CoRR abs/2309.09112 (2023) - [i2]Alexander Brauckmann, Elizabeth Polgreen, Tobias Grosser, Michael F. P. O'Boyle:
mlirSynth: Automatic, Retargetable Program Raising in Multi-Level IR using Program Synthesis. CoRR abs/2310.04196 (2023) - 2021
- [i1]Alexander Brauckmann, Andrés Goens, Jerónimo Castrillón:
A Reinforcement Learning Environment for Polyhedral Optimizations. CoRR abs/2104.13732 (2021)
Coauthor Index
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