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Tag Archives: hybridization
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Paper on speculative refactoring of imperative Deep Learning programs to graph execution directly accepted to ASE ’25
Our paper entitled, “Speculative Automated Refactoring of Imperative Deep Learning Programs to Graph Execution” has been directly accepted to the research papers track at the 2025 IEEE/ACM International Conference on Automated Software Engineering (ASE)! Out of 1190 submissions, 113 were directly accepted, amounting to a 9.5% acceptance rate for directly accepted papers. The conference will take place later this year in Seoul, South Korea. Congratulations to Tatiana, Mehdi, Nan, and Anita!
Paper on refactoring imperative Deep Learning programs to graphs accepted at ASE ’23 NIER
Our paper entitled, “Towards Safe Automated Refactoring of Imperative Deep Learning Programs to Graph Execution” has been accepted to the New Ideas and Emerging Results (NIER) track at the IEEE/ACM 2023 International Conference on Automated Software Engineering (ASE)! Out of 70 papers, 25 were accepted, amounting to a 35.7% acceptance rate. The conference will take place later this year in Kirchberg, Luxembourg.
Congratulations to Tatiana, Mehdi, Nan, and Anita, and thank you for all of your hard work!
Paper on hybridization challenges in imperative Deep Learning programs accepted at MSR ’22
Our paper entitled, “Challenges in migrating imperative Deep Learning programs to graph execution: An empirical study,” has been accepted to the main technical research track at the IEEE/ACM SIGSOFT 2022 International Conference on Mining Software Repositories (MSR)! Out of 138 papers, 45 were accepted, amounting to a 32.6% acceptance rate. The conference will take place later this year in Pittsburgh and is co-located with ICSE 2022.
A special congratulations to Tatiana for publishing her first full conference paper as first-author in the second year of her Ph.D. studies! Also congrats to Mehdi and Anita, and thank you for all of your hard work!
